Machine learning meets pK<sub>a</sub>

Marcel Baltruschat; Paul Czodrowski

doi:10.12688/f1000research.22090.1

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Research Article

Machine learning meets pK_a

[version 1; peer review: 2 approved]

Marcel Baltruschat¹, Paul Czodrowski ¹

PUBLISHED 13 Feb 2020

Author details Author details

¹ Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

Marcel Baltruschat
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Paul Czodrowski
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Cheminformatics gateway.

This article is included in the Artificial Intelligence and Machine Learning gateway.

This article is included in the Mathematical, Physical, and Computational Sciences collection.

This article is included in the Python collection.

Abstract

We present a small molecule pK_a prediction tool entirely written in Python. It predicts the macroscopic pK_a value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r² =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

Keywords

machine learning, pKa value, protonation, dissociation

Corresponding author: Paul Czodrowski

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2020 Baltruschat M and Czodrowski P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Baltruschat M and Czodrowski P. Machine learning meets pK_a [version 1; peer review: 2 approved]. F1000Research 2020, 9(Chem Inf Sci):113 (https://doi.org/10.12688/f1000research.22090.1) First published: 13 Feb 2020, 9(Chem Inf Sci):113 (https://doi.org/10.12688/f1000research.22090.1) Latest published: 27 Apr 2020, 9(Chem Inf Sci):113 (https://doi.org/10.12688/f1000research.22090.2)

Introduction

The acid-base dissociation constant (pK_a) of a drug has a far-reaching influence on pharmacokinetics by altering the solubility, membrane permeability and protein binding affinity of the drug. Several publications summarize these findings in a very comprehensive manner^1–7. An accurate estimation of pK_a values is therefore of utmost importance for successful drug design. Several (commercial and non-commercial) tools and approaches for small molecule pK_a prediction are available: MoKa⁸ uses molecular interaction fields, whereas ACD/Labs Percepta Classic⁹, Marvin¹⁰ and Epik¹¹ make use of the Hammet-Taft equation. By means of Jaguar¹², a quantum-mechanical approach to pK_a prediction becomes possible. Recently, the usage of neural nets for pK_a prediction became prominent^13–15. In particular, the publication by Williams et al.¹⁵ make use of a publicly available data set provided by the application DataWarrior¹⁶ and provides a freely available pK_a prediction tool called OPERA.

As this article is part of a Python collection issue, we provide a pK_a prediction method entirely written in Python¹⁷ and make it available open source (including all data). Our tool computes the macroscopic pK_a value for a monoprotic compound. Our model solely differentiates between a base and acid based on the predicted pK_a value; i.e., we do not offer separate models for acids and bases. In addition to pK_a data from DataWarrior¹⁶, we also employ pK_a data from ChEMBL¹⁸. As external validation sets, we use compound data provided by Novartis¹⁹ and a manually curated data set compiled from literature^20–24, which are not part of the training data.

Methods

Different experimental methods

One crucial point in the field of pK_a measurements (and its usage for pK_a predictions) is linked to the different experimental methods^25,26. Based on the Novartis set, the correlation between capillary electrophoresis and potentiometric measurements (for 15 data points) is convincing enough (mean absolute error (MAE)=0.202, root mean squared error (RMSE)=0.264, correlation coefficient r²=0.981) for us to combine pK_a measurements from these different experimental methods (see Figure 1).

Figure 1. Correlation of Novartis compounds measured in potentiometric and high-throughput (capillary electrophoresis) set-up.

MAE, mean absolute error; RMSE, root mean square error.

We also compare the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources. Due to the missing annotation, it remains unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both). Either way, this comparison is an additional proof-of-concept that the ChEMBL and DataWarrior pK_a data sources can be combined after careful curation. The aforementioned intersection is given in Figure 2. The correlation coefficient between the annotated pK_a values for these two data sets r² is 0.949, the MAE is 0.275, and the RMSE is 0.576.

Figure 2. Intersection between ChEMBL and DataWarrior data sets.

MAE, mean absolute error; RMSE, root mean square error.

Data set preparation

A ChEMBL¹⁸ web search was performed to find all assays containing pK_a measurement data. The following restrictions were made: it must be a physicochemical assay, the measurements must be taken from scientific literature, the assay must be in "small-molecule physicochemical format" and the organism taxonomy must be set to "N/A". This results in a list of 1140 ChEMBL assays downloaded as CSV file. Using a Python script, the CSV file was read in and processed further, extracting all additional information required from an internally hosted copy of the ChEMBL database via SQL. Only pKa measurements, i.e. ChEMBL activities, were taken into account that were specified as exact ("standard_relation" equals "=") and for which one of the following names was specified as "standard_type": "pka", "pka value", "pka1", "pka2", "pka3" or "pka4" (case-insensitive). Measured values for which the molecular structure was not available were also sorted out. The resulting 8111 pK_a measured values were saved as SDF file.

A flat file from DataWarrior¹⁶ named “pKaInWater.dwar” was used in addition. In this case, the file was converted to an SDF file only and contains 7911 entries with valid molecular structures.

These data sets were concatenated for the purpose of this study and preprocessed as follows:

Remove all salts from molecules
Remove molecules containing nitro groups, Bor, Selenium or Silicium
Filter by Lipinski‘s rule of five (one violation allowed)
Keep only pKa data points between 2 and 12
Tautomer standardization of all molecules
Protonate all molecules at pH 7.4
Keep only monoprotic molecules regarding the specified pK_a range
Combine data points from duplicated structures while removing outliers

All steps up to filtering out all pK_a values outside the range of 2 to 12 were performed with Python and RDKit²⁷. The QuacPac²⁸ Tautomers tool from OpenEye was used for tautomer standardization and setting the protonation state to pH 7.4. The Marvin¹⁰ tool from ChemAxon was used to filter out the multiprotic compounds. It predicted the pK_a values of all molecules in the range 2 to 12 and then retained only those molecules where Marvin did not predict more than one pK_a in that range.

The removal of the outliers is performed in two steps. First, before combining multiple measurements for the same molecules, all entries where the pK_a predicted by ChemAxon's Tool Marvin differs from the experimental value by more than four log units are removed. All molecules were then combined on the basis of the isomeric SMILES. In the second step, when combining several measured values of a molecule, all those values that deviate from the mean value by more than two standard deviations are removed. The remaining values are arithmetically averaged.

After all, 5994 unique monoprotic molecules with experimental pK_a values remained. The distribution of pK_a values is given in Figure 3. The same preprocessing steps were also performed on an external test data set provided to us by Novartis¹⁹ (280 molecules) and a manual curation (123 molecules) from literature^20–24.

Figure 3. Distribution of the individual pK_a values.

Learning

First, to simplify cross-validation, a class "CVRegressor" was defined, which can serve as a wrapper for any regressor implementing the Scikit-Learn²⁹ interface. This class simplifies cross-validation itself, training and prediction with the cross-validated model. Next, 196 of the 200 available RDKit descriptors ("MaxPartialCharge", "MinPartialCharge", "MaxAbsPartialCharge" and "MinAbsPartialCharge" were not used because they are computed as "NaN" for many molecules), and a 4096-bit long MorganFeature fingerprint with radius 3 were calculated for the training data set. Random forest (RF), support vector regression (SVR, two configurations), multilayer perceptron (MLP, three configurations) and XGradientBoost (XGB) were used as basic regressors. Unless otherwise specified, the Scikit-Learn default parameters (version 0.22.1) were used. For the RF model, only the number of trees was increased to 1000. For SVR the size of the cache was increased to 4096 megabytes in the first configuration, but this only increases the training speed and has no influence on the model quality. In the second configuration the parameter "gamma" was additionally set to the value "auto". For MLP in the first configuration the number of hidden layers was increased to two and the number of neurons per layer to 500. In the second configuration, early stopping was additionally activated, where 10% of the training data is separated as validation data. If the error of the validation data does not improve by more than 0.001 over ten training epochs, the training is stopped early to avoid overtraining. In the third configuration three hidden layers with a size of 250 neurons each were used with early stopping still activated. For XGB the default parameters of the used library XGBoost (version 0.90)³⁰ were applied. The training of RF, MLP and XGB was parallelized on 12 CPU cores and the generation of the folds for cross-validation as well as the training itself were random seeded to a value of 24 to ensure reproducibility. This results in a total of seven different machine learning configurations.

Six different descriptor/fingerprint combinations were also tested. First only the RDKit descriptors, followed by only the fingerprints and finally both combined. Additionally, all three combinations were tested again in a standardized form (z-transformed). As a result, 42 combinations of regressor and training data configurations were compared.

A 5-fold cross-validation was performed for all configurations, which were evaluated using the MAE, RMSE and the empirical coefficient of determination (r²). After training was completed for all configurations, two external test data sets, which do not contain training data, were used to re-validate each trained cross-validated model. Here, MAE, RMSE, and r² were also calculated as statistical quality measures.

Implementation

The Python dependencies are as follows: Python >= 3.7, NumPy >= 1.18, Scikit-Learn >= 0.22, RDKit >= 2019.09.3, Pandas >= 0.25, XGBoost >= 0.90, JupyterLab >= 1.2, Matplotlib >= 3.1, Seaborn >= 0.9

For the data preparation pipeline, ChemAxon Marvin¹⁰ and OpenEye QUACPAC/Tautomers²⁸ are required. To use the provided prediction model with the included Python script, ChemAxon Marvin¹⁰ is not required.

First of all you need a working Miniconda/Anaconda installation. You can get Miniconda at https://conda.io/en/latest/miniconda.html.

Now you can create an environment named "ml_pka" with all needed dependencies and activate it with:

conda env create -f environment.yml
conda activate ml_pka

You can also create a new environment by yourself and install all dependencies without the environment.yml file:

conda create -n ml_pka python=3.7
conda activate ml_pka

In case of Linux or macOS:

conda install -c defaults -c rdkit -c conda-forge scikit-learn rdkit xgboost jupyterlab matplotlib seaborn

In case of Windows:

conda install -c defaults -c rdkit scikit-learn rdkit jupyterlab matplotlib seaborn
pip install xgboost

Operation

Prediction pipeline. To use the data preparation pipeline you have to be in the repository folder and your conda environment have to be activated. Additionally the Marvin¹⁰ commandline tool cxcalc and the QUACPAC²⁸ commandline tool tautomers have to be set in your PATH variable.

Also the environment variables OE_LICENSE (containing the path to your OpenEye license file) and JAVA_HOME (referring to the Java installation folder, which is needed for cxcalc) have to be set.

After preparation you can display a small usage information with bash run_pipeline.sh -h. Examplary call:

bash run_pipeline.sh --train datasets/chembl25.sdf --test datasets/novartis_cleaned_mono_unique_notraindata.sdf

Prediction tool. First of all you have to be in the repository folder and your conda environment have to be activated. To use the prediction tool you have to retrain the machine learning model. Therefore just call the training script, it will train the 5-fold cross-validated Random Forest machine learning model using 12 cpu cores. If you want to adjust the number of cores you can edit the train_model.py by changing the value of the variable EST_JOBS.

python train_model.py

To use the prediction tool with the trained model QUACPAC/Tautomers have to be available as it was mentioned in the section above.

Now you can call the python script with an SDF file and an output path:

python predict_sdf.py my_test_file.sdf my_output_file.sdf

It should be noted that this model was built for monoprotic structures regarding a pH range of 2 to 12. If the model is used with multiprotic structures, the predicted values will probably not be correct.

Results

The statistics for a five-fold cross-validation are given in Table 1. In terms of the mean absolute error, a random forest with scaled MorganFeatures (radius=3) and descriptors gives the best performing model (MAE=0.682, RMSE=1.032, r²=0.82). For the two external test sets (see Table 2), a random forest with FeatureMorgan (radius=3) gives the best model (Novartis: MAE=1.147, RMSE=1.513, r²=0.569; LiteratureCompilation: MAE=0.532, RMSE=0.785, r²=0.889). The predictive performance for Marvin¹⁰ and the OPERA tool¹⁵ are as follows: Novartis (Marvin: MAE=0.856, RMSE=1.166, r²=0.744; OPERA: MAE= 2.274, RMSE= 3.059, r²= -0.754) and LiteratureComplation^20–24 (Marvin: MAE= 0.566, RMSE= 0.865, r²= 0.866; OPERA: MAE= 1.737, RMSE= 2.182, r²= 0.124). This shows that our model slightly outcompetes Marvin for the LiteratureCompilation, but Marvin performs better for the Novartis dataset. For both data sets, our models¹⁷ have a better predictive performance than the OPERA tool.

Table 1. Statistics of the five-fold cross-validation of the machine learning models.

The two best and worst performing models are highlighted in green and red. For those neural networks where the values were specified as "not available" (“#NA”), the weights could not be optimized properly due to the large value range of the RDKit descriptors, so training failed here.

		Cross-Validation
Modell (seed=24)	Train Configuration	MAE (mean)	MAE (std)	RMSE (mean)	RMSE (std)	R² (mean)	R² (std)
Random Forest (n_estimators=1000)	Desc (196 RDKit)	0,718	0,022	1,077	0,021	0,804	0,01
	FCFP6 (4096 bit)	0,708	0,021	1,094	0,029	0,797	0,008
	Desc + FCFP6	0,683	0,017	1,032	0,013	0,82	0,005
	Desc (196 RDKit) (scaled)	0,717	0,022	1,076	0,022	0,804	0,011
	FCFP6 (4096 bit) (scaled)	0,708	0,021	1,094	0,029	0,797	0,008
	Desc + FCFP6 (scaled)	0,682	0,017	1,032	0,013	0,82	0,005
Support Vector Machine	Desc (196 RDKit)	2,1	0,037	2,436	0,035	-0,004	0,004
	FCFP6 (4096 bit)	0,851	0,025	1,24	0,035	0,74	0,012
	Desc + FCFP6	2,1	0,037	2,436	0,035	-0,004	0,004
	Desc (196 RDKit) (scaled)	0,876	0,033	1,282	0,047	0,722	0,015
	FCFP6 (4096 bit) (scaled)	1,09	0,034	1,466	0,041	0,637	0,014
	Desc + FCFP6 (scaled)	1,02	0,037	1,4	0,047	0,668	0,016
Support Vector Machine (gamma='auto')	Desc (196 RDKit)	2,016	0,042	2,362	0,039	0,056	0,009
	FCFP6 (4096 bit)	1,612	0,031	1,926	0,033	0,373	0,007
	Desc + FCFP6	1,642	0,061	2,052	0,06	0,288	0,027
	Desc (196 RDKit) (scaled)	0,882	0,035	1,288	0,048	0,719	0,016
	FCFP6 (4096 bit) (scaled)	1,09	0,034	1,465	0,041	0,637	0,014
	Desc + FCFP6 (scaled)	1,019	0,037	1,4	0,047	0,669	0,016
Multilayer Perceptron (hidden_layer_sizes=(500, 500))	Desc (196 RDKit)	#NA	#NA	#NA	#NA	#NA	#NA
	FCFP6 (4096 bit)	0,866	0,025	1,27	0,047	0,727	0,019
	Desc + FCFP6	#NA	#NA	#NA	#NA	#NA	#NA
	Desc (196 RDKit) (scaled)	0,726	0,018	1,102	0,05	0,794	0,022
	FCFP6 (4096 bit) (scaled)	1,037	0,045	1,457	0,057	0,64	0,024
	Desc + FCFP6 (scaled)	0,968	0,032	1,383	0,04	0,677	0,014
Multilayer Perceptron (hidden_layer_sizes=(500, 500), early_stopping=True)	Desc (196 RDKit)	#NA	#NA	#NA	#NA	#NA	#NA
	FCFP6 (4096 bit)	0,894	0,024	1,297	0,04	0,715	0,016
	Desc + FCFP6	#NA	#NA	#NA	#NA	#NA	#NA
	Desc (196 RDKit) (scaled)	0,768	0,034	1,161	0,09	0,77	0,038
	FCFP6 (4096 bit) (scaled)	1,031	0,037	1,447	0,057	0,645	0,026
	Desc + FCFP6 (scaled)	0,984	0,029	1,404	0,035	0,666	0,017
Multilayer Perceptron (hidden_layer_sizes=(250, 250, 250), early_stopping=True)	Desc (196 RDKit)	#NA	#NA	#NA	#NA	#NA	#NA
	FCFP6 (4096 bit)	0,869	0,023	1,265	0,039	0,729	0,016
	Desc + FCFP6	#NA	#NA	#NA	#NA	#NA	#NA
	Desc (196 RDKit) (scaled)	0,775	0,008	1,158	0,033	0,773	0,013
	FCFP6 (4096 bit) (scaled)	1,026	0,038	1,455	0,053	0,642	0,022
	Desc + FCFP6 (scaled)	0,973	0,035	1,388	0,053	0,674	0,023
XGBoost	Desc (196 RDKit)	1,02	0,014	1,353	0,021	0,691	0,007
	FCFP6 (4096 bit)	1,094	0,027	1,423	0,036	0,657	0,011
	Desc + FCFP6	1,018	0,01	1,346	0,022	0,694	0,005
	Desc (196 RDKit) (scaled)	1,02	0,014	1,353	0,021	0,691	0,007
	FCFP6 (4096 bit) (scaled)	1,094	0,027	1,423	0,036	0,657	0,011
	Desc + FCFP6 (scaled)	1,018	0,01	1,346	0,022	0,694	0,005

MAE, mean absolute error; RMSE, root mean square error.

Table 2. Predictive performance of the machine learning models the on the two external test sets.

		Novartis			AvLiLuMoVe
Modell (seed=24)	Train Configuration	MAE	RMSE	R²	MAE	RMSE	R²
Random Forest (n_estimators=1000)	Desc (196 RDKit)	1,259	1,607	0,513	0,689	0,979	0,828
	FCFP6 (4096 bit)	1,147	1,513	0,569	0,532	0,785	0,889
	Desc + FCFP6	1,2	1,532	0,558	0,628	0,884	0,86
	Desc (196 RDKit) (scaled)	1,259	1,607	0,513	0,688	0,979	0,828
	FCFP6 (4096 bit) (scaled)	1,147	1,513	0,569	0,532	0,785	0,889
	Desc + FCFP6 (scaled)	1,198	1,531	0,558	0,628	0,884	0,86
Support Vector Machine	Desc (196 RDKit)	2,177	2,451	-0,132	2,18	2,441	-0,07
	FCFP6 (4096 bit)	1,423	1,732	0,435	0,688	0,981	0,827
	Desc + FCFP6	2,177	2,451	-0,132	2,18	2,441	-0,07
	Desc (196 RDKit) (scaled)	1,382	1,735	0,433	0,772	1,058	0,799
	FCFP6 (4096 bit) (scaled)	1,771	2,035	0,219	1,115	1,422	0,637
	Desc + FCFP6 (scaled)	1,746	2,015	0,235	1,044	1,345	0,675
Support Vector Machine (gamma='auto')	Desc (196 RDKit)	2,162	2,428	-0,111	1,921	2,242	0,097
	FCFP6 (4096 bit)	1,686	1,932	0,297	1,429	1,67	0,499
	Desc + FCFP6	2,161	2,442	-0,124	1,611	2,004	0,279
	Desc (196 RDKit) (scaled)	1,378	1,732	0,435	0,766	1,049	0,802
	FCFP6 (4096 bit) (scaled)	1,77	2,034	0,22	1,114	1,421	0,637
	Desc + FCFP6 (scaled)	1,744	2,013	0,236	1,043	1,343	0,676
Multilayer Perceptron (hidden_layer_sizes=(500, 500))	Desc (196 RDKit)	#NV	#NV	#NV	#NV	#NV	#NV
	FCFP6 (4096 bit)	1,414	1,773	0,407	0,852	1,169	0,755
	Desc + FCFP6	#NV	#NV	#NV	#NV	#NV	#NV
	Desc (196 RDKit) (scaled)	1,318	1,634	0,497	0,688	0,942	0,841
	FCFP6 (4096 bit) (scaled)	1,627	2,033	0,221	1,102	1,569	0,558
	Desc + FCFP6 (scaled)	1,542	1,941	0,29	1,001	1,427	0,634
Multilayer Perceptron (hidden_layer_sizes=(500, 500), early_stopping=True)	Desc (196 RDKit)	#NV	#NV	#NV	#NV	#NV	#NV
	FCFP6 (4096 bit)	1,404	1,772	0,408	0,846	1,154	0,761
	Desc + FCFP6	#NV	#NV	#NV	#NV	#NV	#NV
	Desc (196 RDKit) (scaled)	1,298	1,626	0,502	0,701	0,936	0,843
	FCFP6 (4096 bit) (scaled)	1,611	2,028	0,225	1,141	1,575	0,554
	Desc + FCFP6 (scaled)	1,605	1,998	0,248	0,987	1,365	0,665
Multilayer Perceptron (hidden_layer_sizes=(250, 250, 250), early_stopping=True)	Desc (196 RDKit)	#NV	#NV	#NV	#NV	#NV	#NV
	FCFP6 (4096 bit)	1,363	1,717	0,445	0,86	1,164	0,757
	Desc + FCFP6	#NV	#NV	#NV	#NV	#NV	#NV
	Desc (196 RDKit) (scaled)	1,354	1,705	0,452	0,777	1,057	0,799
	FCFP6 (4096 bit) (scaled)	1,584	1,989	0,254	1,053	1,468	0,613
	Desc + FCFP6 (scaled)	1,581	1,963	0,274	0,953	1,352	0,672
XGBoost	Desc (196 RDKit)	1,367	0,453	1,704	0,819	0,806	1,04
	FCFP6 (4096 bit)	1,28	0,503	1,624	0,782	0,823	0,992
	Desc + FCFP6	1,293	0,495	1,637	0,774	0,822	0,995
	Desc (196 RDKit) (scaled)	1,367	0,453	1,704	0,819	0,806	1,04
	FCFP6 (4096 bit) (scaled)	1,28	0,503	1,624	0,782	0,823	0,992
	Desc + FCFP6 (scaled)	1,293	0,495	1,637	0,774	0,822	0,995
ChemAxon Marvin (V20.1.0)		0,856	1,166	0,744	0,566	0,865	0,866
OPERA (V2.5)*		2,274	3,059	-0,754	1,737	2,182	0,124

*For OPERA 6 molecules from AvLiLuMoVe and 31 molecules from Novartis were left out because OPERA predicted either two or zero pK_a values.

MAE, mean absolute error; RMSE, root mean square error.

Discussion and conclusions

The good performance of Marvin on the Novartis set is interesting to note: the RMSE is almost 0.4 units better than our top performing model. This could be because Marvin’s training set is much larger than our own training set. This provides a better foundation for the training of the Marvin model. In contrast, Marvin performs slightly worse than our top model on the LiteratureCompilation. The OPERA tool performs significantly worse than our model on both external test sets. We assume that the addition of 2470 ChEMBL pKa-datapoints to our training set which are not part of the OPERA training set leads to this drop in predictive performance. In addition, the pre-processing of the data is performed differently by OPERA in comparison to our pre-processing procedure.

As next step for the enhancement and improvement of our pK_a prediction model¹⁷, we are currently expanding it to multiprotic molecules. We are also investigating the impact of different neural net architectures and types (such as graph neural nets) and the development of individual models for acids and bases. From a chemistry perspective, an analysis of pK_a effects of different functional groups (e.g. by means of matched molecular pairs analysis) is an on-going effort for a future publication.

Data availability

Source data

Zenodo: czodrowskilab/Machine-learning-meets-pKa article. https://doi.org/10.5281/zenodo.3662245¹⁷.

The following data sets were used in this study:

AvLiLuMoVe.sdf - Manually combined literature pKa data.
chembl25.sdf - Experimental pKa data extracted from ChEMBL25.
datawarrior.sdf - pKa data shipped with DataWarrior.
combined_training_datasets_unique.sdf - Preprocessed and combined data from datasets chembl25.sdf and datawarrior.sdf, used as training dataset.
AvLiLuMoVe_cleaned_mono_unique_notraindata.sdf - used as external testset.
novartis_cleaned_mono_unique_notraindata.sdf - inhouse dataset provided by Novartis¹⁹, used as external testset.

The data sets are also available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

License: MIT license.

Software availability

The source code is available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.3662245¹⁷.

License: MIT license.

Acknowledgments

Ed Griffen (MedChemica) is acknowledged for his investigations on our initial data set which revealed some wrongly annotated data points. We thank Bob Clark, Eric Jamois and Michael Lawless (Simulations Plus) for their support and advise in terms of data curation. Alpha Lee and Matt Robinson (Cambridge University) are appreciated for fruitful discussions.

Faculty Opinions recommended

References

1. Manallack DT: The PK_a Distribution of Drugs: Application to Drug Discovery. Perspect Medicin Chem. 2007; 1: 25–38. PubMed Abstract | Publisher Full Text | Free Full Text
2. Manallack DT, Yuriev E, Chalmers DK: The influence and manipulation of acid/base properties in drug discovery. Drug Discov Today Technol. 2018; 27: 41–47. PubMed Abstract | Publisher Full Text
3. Manallack DT, Prankerd RJ, Yuriev E, et al.: The significance of acid/base properties in drug discovery. Chem Soc Rev. 2013; 42(2): 485–496. PubMed Abstract | Publisher Full Text | Free Full Text
4. Meanwell NA: Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol. 2011; 24(9): 1420–1456. PubMed Abstract | Publisher Full Text
5. Gleeson MP: Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem. 2008; 51(4): 817–834. PubMed Abstract | Publisher Full Text
6. Leeson PD, St-Gallay SA, Wenlock MC: Impact of Ion Class and Time on Oral Drug Molecular Properties. Med Chem Commun. 2011; 2(2): 91–105. Publisher Full Text
7. Charifson PS, Walters WP: Acidic and basic drugs in medicinal chemistry: a perspective. J Med Chem. 2014; 57(23): 9701–9717. PubMed Abstract | Publisher Full Text
8. Milletti F, Storchi L, Sforna G, et al.: New and original pK_a prediction method using grid molecular interaction fields. J Chem Inf Model. 2007; 47(6): 2172–2181. PubMed Abstract | Publisher Full Text
9. ACD/Percepta, Advanced Chemistry Development, Inc.. Toronto, On Canada, Www.Acdlabs.Com, 2019. Reference Source
10. Marvin 20.1.0, 2020. ChemAxon. Reference Source
11. Shelley JC, Cholleti A, Frye LL, et al.: Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des. 2007; 21(12): 681–691. PubMed Abstract | Publisher Full Text
12. Bochevarov AD, Watson MA, Greenwood JR, et al.: Multiconformation, Density Functional Theory-Based pK_a Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups. J Chem Theory Comput. 2016; 12(12): 6001–6019. PubMed Abstract | Publisher Full Text
13. Fraczkiewicz R, Lobell M, Göller AH, et al.: Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pK_a prediction. J Chem Inf Model. 2015; 55(2): 389–397. PubMed Abstract | Publisher Full Text
14. Roszak R, Beker W, Molga K, et al.: Rapid and Accurate Prediction of pK_a Values of C-H Acids Using Graph Convolutional Neural Networks. J Am Chem Soc. 2019; 141(43): 17142–17149. PubMed Abstract | Publisher Full Text
15. Mansouri K, Cariello NF, Korotcov A, et al.: Open-Source QSAR Models for PKa Prediction Using Multiple Machine Learning Approaches. J Cheminform. 2019; 11(1): 60. Publisher Full Text | Free Full Text
16. Sander T, Freyss J, von Korff M, et al.: DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015; 55(2): 460–473. PubMed Abstract | Publisher Full Text
17. Lewis RA, Rodde S: Novartis Pharma AG. Basel, Switzerland.
18. Settimo L, Bellman K, Knegtel RM: Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm Res. 2014; 31(4): 1082–1095. PubMed Abstract | Publisher Full Text
19. Liao C, Nicklaus MC: Comparison of nine programs predicting pK_a values of pharmaceutical substances. J Chem Inf Model. 2009; 49(12): 2801–2812. PubMed Abstract | Publisher Full Text
20. Avdeef A: Absorption and Drug Development: Solubility, Permeability, and Charge State. John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2012. Publisher Full Text
21. Morgenthaler M, Schweizer E, Hoffmann-Röder A, et al.: Predicting and tuning physicochemical properties in lead optimization: amine basicities. ChemMedChem. 2007; 2(8): 1100–1115. PubMed Abstract | Publisher Full Text
22. Luan F, Ma W, Zhang H, et al.: Prediction of pK_a for neutral and basic drugs based on radial basis function Neural networks and the heuristic method. Pharm Res. 2005; 22(9): 1454–1460. PubMed Abstract | Publisher Full Text
23. Dardonville C: Automated techniques in pK_a determination: low medium and high-throughput screening methods. Drug Discov Today Technol. 2018; 27: 49–58. PubMed Abstract | Publisher Full Text
24. Reijenga J, van Hoof A, van Loon A, et al.: Development of Methods for the Determination of pK_a Values. Anal Chem Insights. 2013; 8(1): 53–71. PubMed Abstract | Publisher Full Text | Free Full Text
25. RDKit, Open-Source Chemiformatics. Reference Source
26. QUACPAC 2.0.2.2: OpenEye Scientific Software, Santa Fe, NM. Reference Source
27. Pedregosa F, Weiss R, Brucher M: Scikit-Learn: Machine Learning in Python. J Mach Learn Res. 2011; 12: 2825–2830. Reference Source
28. Chen T, Guestrin C: XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’ 16; ACM Press: New York, New York, USA. 2016; 785–794. Publisher Full Text
29. Baltruschat M, czodrowskilab, Czodrowski P: czodrowskilab/Machine-learning-meets-pKa article (Version article). Zenodo. 2020. http://www.doi.org/10.5281/zenodo.3662245
30. Gaulton A, Hersey A, Nowotka M, et al.: The ChEMBL database in 2017. Nucleic Acids Res. 2017; 45(D1): D945–D954. PubMed Abstract | Publisher Full Text | Free Full Text

Comments on this article Comments (2)

Version 2

VERSION 2 PUBLISHED 27 Apr 2020

Revised

Comment

Version 1

VERSION 1 PUBLISHED 13 Feb 2020

Discussion is closed on this version, please comment on the latest version above.

Author Response 12 Mar 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

12 Mar 2020

Author Response

Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of ... Continue reading Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of the molecules to be predicted with respect to the applicability of the trained model is not implemented, because otherwise the Marvin tool from ChemAxon would also be required for the prediction. With Marvin the classification between mono- and multiprotic molecules is performed. However, we are considering adding an optional validation option for the input structures.
Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of the molecules to be predicted with respect to the applicability of the trained model is not implemented, because otherwise the Marvin tool from ChemAxon would also be required for the prediction. With Marvin the classification between mono- and multiprotic molecules is performed. However, we are considering adding an optional validation option for the input structures.
Competing Interests: No competing interests were disclosed. Close
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Reader Comment 20 Feb 2020

Kamel Mansouri, ILS, USA

20 Feb 2020

Reader Comment

This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source ... Continue reading This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source pKa predictors such as OPERA.
The authors clearly state that their tool only works on monoprotic chemicals and doesn't differentiate between acidic and basic pKa. However, in their comparison with other tools that do, the authors fail to make that differentiation. To compare the comparable, the authors should compare their predictions with OPERA predictions separately for acidic and basic pKa also because one of their data sources was DataWarrior (also used for OPERA training and testing) differentiates between the two (acidic Vs basic).

Since the model only works for monoprotic chemicals, is there an applicability domain assessment or some sort of error message that informs the user when they try to use for a multiprotic chemical? if not, it should be added.

Both the results and discussion sections should expanded further and clarify the strengths and limitations of the tool.
This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source pKa predictors such as OPERA.
The authors clearly state that their tool only works on monoprotic chemicals and doesn't differentiate between acidic and basic pKa. However, in their comparison with other tools that do, the authors fail to make that differentiation. To compare the comparable, the authors should compare their predictions with OPERA predictions separately for acidic and basic pKa also because one of their data sources was DataWarrior (also used for OPERA training and testing) differentiates between the two (acidic Vs basic).

Since the model only works for monoprotic chemicals, is there an applicability domain assessment or some sort of error message that informs the user when they try to use for a multiprotic chemical? if not, it should be added.

Both the results and discussion sections should expanded further and clarify the strengths and limitations of the tool.
Competing Interests: I have no competing interests. Close
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Discussion is closed on this version, please comment on the latest version above.

Author details Author details

¹ Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

Paul Czodrowski
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

version 2

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Published: 27 Apr 2020, 9:113

https://doi.org/10.12688/f1000research.22090.2

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Published: 13 Feb 2020, 9:113

https://doi.org/10.12688/f1000research.22090.1

© 2020 Baltruschat M and Czodrowski P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Baltruschat M and Czodrowski P. Machine learning meets pK_a [version 1; peer review: 2 approved]. F1000Research 2020, 9(Chem Inf Sci):113 (https://doi.org/10.12688/f1000research.22090.1)

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Reviewer Report 24 Mar 2020

Johannes Kirchmair, Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria

Approved

https://doi.org/10.5256/f1000research.24362.r61511

Baltruschat and Czodrowski report on the development of a Python-based tool for the prediction of pk_a values. The tool is made available open source and will certainly be useful to the scientific community.

Could the authors

Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
Please revise the sentence starting with "We also compare the overlap...".
"make use of a" should be replaced by "makes use of a".
Please explain where DataWarrior's data originates from.
Please comment on the outliers observed in Figure 2.
Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
"isomeric SMILES": is it canonical as well?
Avoid using "you" in the Implementation section (use "one [can]..." instead).
The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: cheminformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response 27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

27 Apr 2020

Author Response

Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto ... Continue reading Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
We have included new figures (2A and 2B) with pairwise Tanimoto coefficients (4096 bit MorganFeatures radius=3).

Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
A thorough test of the statistical significance is hindered by the fact the external open source tool OPERA cannot process all molecules of our external test sets. We therefore decided to rephrase “This showed that our model slightly outcompeted Marvin for the LiteratureCompilation” to “his showed that our model has a slightly better performance than Marvin for the LiteratureCompilation”.

In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
The open source model is now mentioned in the abstract.

It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
We mention this already in the section “Different experimental methods” :“Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both)”.

Please revise the sentence starting with "We also compare the overlap...".
Changed "We also compared the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources." To "We also compared the pKa values of 187 monoprotic molecules contained in both the ChEMBL and DataWarrior data sets."

"make use of a" should be replaced by "makes use of a".
Done

Please explain where DataWarrior's data originates from.
We cannot make any statement about the origin, because it is not included in the DataWarrior file.

Please comment on the outliers observed in Figure 2.
We include the name of the outliers and the different pKa values originating from different data sources, if they differ by more than two log units.

Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
To ensure that no training data was contained in the test data sets, the canonical isomeric SMILES were checked for matches in both training and test data sets and corresponding hits were removed from the test data sets. Chirality was not included in this comparison.

"isomeric SMILES": is it canonical as well?
Yes, it is the canonical isomeric SMILES generated by RDKit.

Avoid using "you" in the Implementation section (use "one [can]..." instead).
Done

The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?
We have pulled most of the numbers apart and included bullet points which improves the readability and makes a comparison more feasible.
Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
We have included new figures (2A and 2B) with pairwise Tanimoto coefficients (4096 bit MorganFeatures radius=3).

Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
A thorough test of the statistical significance is hindered by the fact the external open source tool OPERA cannot process all molecules of our external test sets. We therefore decided to rephrase “This showed that our model slightly outcompeted Marvin for the LiteratureCompilation” to “his showed that our model has a slightly better performance than Marvin for the LiteratureCompilation”.

In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
The open source model is now mentioned in the abstract.

It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
We mention this already in the section “Different experimental methods” :“Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both)”.

Please revise the sentence starting with "We also compare the overlap...".
Changed "We also compared the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources." To "We also compared the pKa values of 187 monoprotic molecules contained in both the ChEMBL and DataWarrior data sets."

"make use of a" should be replaced by "makes use of a".
Done

Please explain where DataWarrior's data originates from.
We cannot make any statement about the origin, because it is not included in the DataWarrior file.

Please comment on the outliers observed in Figure 2.
We include the name of the outliers and the different pKa values originating from different data sources, if they differ by more than two log units.

Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
To ensure that no training data was contained in the test data sets, the canonical isomeric SMILES were checked for matches in both training and test data sets and corresponding hits were removed from the test data sets. Chirality was not included in this comparison.

"isomeric SMILES": is it canonical as well?
Yes, it is the canonical isomeric SMILES generated by RDKit.

Avoid using "you" in the Implementation section (use "one [can]..." instead).
Done

The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?
We have pulled most of the numbers apart and included bullet points which improves the readability and makes a comparison more feasible.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

27 Apr 2020

Author Response

Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto ... Continue reading Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
We have included new figures (2A and 2B) with pairwise Tanimoto coefficients (4096 bit MorganFeatures radius=3).

Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
A thorough test of the statistical significance is hindered by the fact the external open source tool OPERA cannot process all molecules of our external test sets. We therefore decided to rephrase “This showed that our model slightly outcompeted Marvin for the LiteratureCompilation” to “his showed that our model has a slightly better performance than Marvin for the LiteratureCompilation”.

In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
The open source model is now mentioned in the abstract.

It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
We mention this already in the section “Different experimental methods” :“Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both)”.

Please revise the sentence starting with "We also compare the overlap...".
Changed "We also compared the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources." To "We also compared the pKa values of 187 monoprotic molecules contained in both the ChEMBL and DataWarrior data sets."

"make use of a" should be replaced by "makes use of a".
Done

Please explain where DataWarrior's data originates from.
We cannot make any statement about the origin, because it is not included in the DataWarrior file.

Please comment on the outliers observed in Figure 2.
We include the name of the outliers and the different pKa values originating from different data sources, if they differ by more than two log units.

Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
To ensure that no training data was contained in the test data sets, the canonical isomeric SMILES were checked for matches in both training and test data sets and corresponding hits were removed from the test data sets. Chirality was not included in this comparison.

"isomeric SMILES": is it canonical as well?
Yes, it is the canonical isomeric SMILES generated by RDKit.

Avoid using "you" in the Implementation section (use "one [can]..." instead).
Done

The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?
We have pulled most of the numbers apart and included bullet points which improves the readability and makes a comparison more feasible.
Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
We have included new figures (2A and 2B) with pairwise Tanimoto coefficients (4096 bit MorganFeatures radius=3).

Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
A thorough test of the statistical significance is hindered by the fact the external open source tool OPERA cannot process all molecules of our external test sets. We therefore decided to rephrase “This showed that our model slightly outcompeted Marvin for the LiteratureCompilation” to “his showed that our model has a slightly better performance than Marvin for the LiteratureCompilation”.

In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
The open source model is now mentioned in the abstract.

It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
We mention this already in the section “Different experimental methods” :“Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both)”.

Please revise the sentence starting with "We also compare the overlap...".
Changed "We also compared the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources." To "We also compared the pKa values of 187 monoprotic molecules contained in both the ChEMBL and DataWarrior data sets."

"make use of a" should be replaced by "makes use of a".
Done

Please explain where DataWarrior's data originates from.
We cannot make any statement about the origin, because it is not included in the DataWarrior file.

Please comment on the outliers observed in Figure 2.
We include the name of the outliers and the different pKa values originating from different data sources, if they differ by more than two log units.

Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
To ensure that no training data was contained in the test data sets, the canonical isomeric SMILES were checked for matches in both training and test data sets and corresponding hits were removed from the test data sets. Chirality was not included in this comparison.

"isomeric SMILES": is it canonical as well?
Yes, it is the canonical isomeric SMILES generated by RDKit.

Avoid using "you" in the Implementation section (use "one [can]..." instead).
Done

The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?
We have pulled most of the numbers apart and included bullet points which improves the readability and makes a comparison more feasible.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 25 Feb 2020

Ruth Brenk, Department of Biomedicine, University of Bergen, Bergen, Norway

Approved

https://doi.org/10.5256/f1000research.24362.r60011

The article describes a machine learning method for pkA prediction. The presented method and results are scientifically solid. Therefore, I recommend the article for indexing. However, the methods and results should be presented more clearly before the article is indexed.
... Continue reading

Past tense should be used consistently to describe the methods and results.
The “different experimental methods” should be moved to the results part as it contains results.
A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?
Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?
In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: chemoinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

27 Apr 2020

Author Response
1. Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.
2. The “different experimental methods” should be moved
... Continue reading
Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.

The “different experimental methods” should be moved to the results part as it contains results.This part was moved accordingly.

A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?We have added this information in the manuscript: "The Novartis data set consists of 280 unique molecules with a molecular weight between 129 and 670 daltons (mean value 348.68, standard deviation 94.17). The calculated LogP values vary between -1.54 and 6.30 (mean value 3.01, standard deviation 1.41). The 280 molecules spread over 228 unique Murcko Scaffolds. The ten most common murcko scaffolds cover 15% of the molecules of the total data set (42/280)."

Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?The user has to train the model again by himself or herself, because the model is larger than 1.7 GB as a file and therefore cannot be offered for download without paid services. Even with the best possible bzip2 compression, the file is still over 250 MB in size and thus larger than the 100 MB limit at GitHub. In addition, if the model is provided as a file, you must use exactly the same versions of the Python libraries used for training to load the model, otherwise inconsistencies may occur. However, if the model is trained again by yourself, only the minimum library requirements have to be met.

In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.This was done and is reflected in the new manuscript version.
Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.

The “different experimental methods” should be moved to the results part as it contains results.This part was moved accordingly.

A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?We have added this information in the manuscript: "The Novartis data set consists of 280 unique molecules with a molecular weight between 129 and 670 daltons (mean value 348.68, standard deviation 94.17). The calculated LogP values vary between -1.54 and 6.30 (mean value 3.01, standard deviation 1.41). The 280 molecules spread over 228 unique Murcko Scaffolds. The ten most common murcko scaffolds cover 15% of the molecules of the total data set (42/280)."

Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?The user has to train the model again by himself or herself, because the model is larger than 1.7 GB as a file and therefore cannot be offered for download without paid services. Even with the best possible bzip2 compression, the file is still over 250 MB in size and thus larger than the 100 MB limit at GitHub. In addition, if the model is provided as a file, you must use exactly the same versions of the Python libraries used for training to load the model, otherwise inconsistencies may occur. However, if the model is trained again by yourself, only the minimum library requirements have to be met.

In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.This was done and is reflected in the new manuscript version.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

27 Apr 2020

Author Response
1. Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.
2. The “different experimental methods” should be moved
... Continue reading
Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.

The “different experimental methods” should be moved to the results part as it contains results.This part was moved accordingly.

A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?We have added this information in the manuscript: "The Novartis data set consists of 280 unique molecules with a molecular weight between 129 and 670 daltons (mean value 348.68, standard deviation 94.17). The calculated LogP values vary between -1.54 and 6.30 (mean value 3.01, standard deviation 1.41). The 280 molecules spread over 228 unique Murcko Scaffolds. The ten most common murcko scaffolds cover 15% of the molecules of the total data set (42/280)."

Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?The user has to train the model again by himself or herself, because the model is larger than 1.7 GB as a file and therefore cannot be offered for download without paid services. Even with the best possible bzip2 compression, the file is still over 250 MB in size and thus larger than the 100 MB limit at GitHub. In addition, if the model is provided as a file, you must use exactly the same versions of the Python libraries used for training to load the model, otherwise inconsistencies may occur. However, if the model is trained again by yourself, only the minimum library requirements have to be met.

In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.This was done and is reflected in the new manuscript version.
Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.

The “different experimental methods” should be moved to the results part as it contains results.This part was moved accordingly.

A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?We have added this information in the manuscript: "The Novartis data set consists of 280 unique molecules with a molecular weight between 129 and 670 daltons (mean value 348.68, standard deviation 94.17). The calculated LogP values vary between -1.54 and 6.30 (mean value 3.01, standard deviation 1.41). The 280 molecules spread over 228 unique Murcko Scaffolds. The ten most common murcko scaffolds cover 15% of the molecules of the total data set (42/280)."

Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?The user has to train the model again by himself or herself, because the model is larger than 1.7 GB as a file and therefore cannot be offered for download without paid services. Even with the best possible bzip2 compression, the file is still over 250 MB in size and thus larger than the 100 MB limit at GitHub. In addition, if the model is provided as a file, you must use exactly the same versions of the Python libraries used for training to load the model, otherwise inconsistencies may occur. However, if the model is trained again by yourself, only the minimum library requirements have to be met.

In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.This was done and is reflected in the new manuscript version.
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Version 2

VERSION 2 PUBLISHED 27 Apr 2020

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Version 1

VERSION 1 PUBLISHED 13 Feb 2020

Discussion is closed on this version, please comment on the latest version above.

Author Response 12 Mar 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

12 Mar 2020

Author Response

Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of ... Continue reading Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of the molecules to be predicted with respect to the applicability of the trained model is not implemented, because otherwise the Marvin tool from ChemAxon would also be required for the prediction. With Marvin the classification between mono- and multiprotic molecules is performed. However, we are considering adding an optional validation option for the input structures.
Thanks for the comments, we will take a closer look at the distinction between acidic and basic pKa and the different OPERA models in a follow-up publication.

Currently, a check of the molecules to be predicted with respect to the applicability of the trained model is not implemented, because otherwise the Marvin tool from ChemAxon would also be required for the prediction. With Marvin the classification between mono- and multiprotic molecules is performed. However, we are considering adding an optional validation option for the input structures.
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Reader Comment 20 Feb 2020

Kamel Mansouri, ILS, USA

20 Feb 2020

Reader Comment

This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source ... Continue reading This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source pKa predictors such as OPERA.
The authors clearly state that their tool only works on monoprotic chemicals and doesn't differentiate between acidic and basic pKa. However, in their comparison with other tools that do, the authors fail to make that differentiation. To compare the comparable, the authors should compare their predictions with OPERA predictions separately for acidic and basic pKa also because one of their data sources was DataWarrior (also used for OPERA training and testing) differentiates between the two (acidic Vs basic).

Since the model only works for monoprotic chemicals, is there an applicability domain assessment or some sort of error message that informs the user when they try to use for a multiprotic chemical? if not, it should be added.

Both the results and discussion sections should expanded further and clarify the strengths and limitations of the tool.
This is a good attempt to model pKa which is an important parameter to predict. This type of work is also a good addition to the existing free and open-source pKa predictors such as OPERA.
The authors clearly state that their tool only works on monoprotic chemicals and doesn't differentiate between acidic and basic pKa. However, in their comparison with other tools that do, the authors fail to make that differentiation. To compare the comparable, the authors should compare their predictions with OPERA predictions separately for acidic and basic pKa also because one of their data sources was DataWarrior (also used for OPERA training and testing) differentiates between the two (acidic Vs basic).

Since the model only works for monoprotic chemicals, is there an applicability domain assessment or some sort of error message that informs the user when they try to use for a multiprotic chemical? if not, it should be added.

Both the results and discussion sections should expanded further and clarify the strengths and limitations of the tool.
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Version 2 (revision) 27 Apr 20
Version 1 13 Feb 20	read	read

Ruth Brenk, University of Bergen, Bergen, Norway
Johannes Kirchmair, University of Vienna, Vienna, Austria

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24 Mar 2020 | for Version 1

Johannes Kirchmair, Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria

25 Views Cite this report Responses(1)

Approved

Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
Please revise the sentence starting with "We also compare the overlap...".
"make use of a" should be replaced by "makes use of a".
Please explain where DataWarrior's data originates from.
Please comment on the outliers observed in Figure 2.
Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
"isomeric SMILES": is it canonical as well?
Avoid using "you" in the Implementation section (use "one [can]..." instead).
The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

cheminformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response

27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

Could the authors please comment on the structural relationship between the training and the test data (how far apart are these datasets, e.g. measured by the distribution of pairwise Tanimoto coefficients based on molecular fingerprints)?
We have included new figures (2A and 2B) with pairwise Tanimoto coefficients (4096 bit MorganFeatures radius=3).

Could the authors please confirm statistical significance of the key statements made in the first paragraph of the Results section, starting with "This shows that our model slightly outcompetes..."?
A thorough test of the statistical significance is hindered by the fact the external open source tool OPERA cannot process all molecules of our external test sets. We therefore decided to rephrase “This showed that our model slightly outcompeted Marvin for the LiteratureCompilation” to “his showed that our model has a slightly better performance than Marvin for the LiteratureCompilation”.

In the Abstract, please name the open source model that was tested as part of the comparative performance analysis.
The open source model is now mentioned in the abstract.

It is not clear from the manuscript text whether capillary electrophoresis and potentiometric measurements were the only two experimental methods considered in this work (or, for the Novartis dataset only).
We mention this already in the section “Different experimental methods” :“Due to the missing annotation, it remained unclear if different experimental methods were used or multiple measurements with the same experimental method have been performed (or a mixture of both)”.

Please revise the sentence starting with "We also compare the overlap...".
Changed "We also compared the overlap of the filtered (see next section) ChEMBL and DataWarrior data sets, 187 monoprotic molecules could be identified in both sources." To "We also compared the pKa values of 187 monoprotic molecules contained in both the ChEMBL and DataWarrior data sets."

"make use of a" should be replaced by "makes use of a".
Done

Please explain where DataWarrior's data originates from.
We cannot make any statement about the origin, because it is not included in the DataWarrior file.

Please comment on the outliers observed in Figure 2.
We include the name of the outliers and the different pKa values originating from different data sources, if they differ by more than two log units.

Please explain the procedure that was followed to ensure that there was no overlap between the training data and the external validation sets (the Novartis dataset in particular).
To ensure that no training data was contained in the test data sets, the canonical isomeric SMILES were checked for matches in both training and test data sets and corresponding hits were removed from the test data sets. Chirality was not included in this comparison.

"isomeric SMILES": is it canonical as well?
Yes, it is the canonical isomeric SMILES generated by RDKit.

Avoid using "you" in the Implementation section (use "one [can]..." instead).
Done

The part starting with "For the two external test sets..." is difficult to read. Could the authors find a more simple way to describe this and enable readers to compare the individual values more easily?
We have pulled most of the numbers apart and included bullet points which improves the readability and makes a comparison more feasible.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

33 Views

25 Feb 2020 | for Version 1

Ruth Brenk, Department of Biomedicine, University of Bergen, Bergen, Norway

33 Views Cite this report Responses(1)

Approved

Past tense should be used consistently to describe the methods and results.
The “different experimental methods” should be moved to the results part as it contains results.
A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?
Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?
In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

chemoinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

27 Apr 2020

Paul Czodrowski, Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany

Past tense should be used consistently to describe the methods and results.We have used past tense throughout the entire document.
The “different experimental methods” should be moved to the results part as it contains results.This part was moved accordingly.
A short description of the molecules in the self-compiled data set and the Novartis set should be added. What type of molecules are contained in these sets?We have added this information in the manuscript: "The Novartis data set consists of 280 unique molecules with a molecular weight between 129 and 670 daltons (mean value 348.68, standard deviation 94.17). The calculated LogP values vary between -1.54 and 6.30 (mean value 3.01, standard deviation 1.41). The 280 molecules spread over 228 unique Murcko Scaffolds. The ten most common murcko scaffolds cover 15% of the molecules of the total data set (42/280)."
Why does the user need to train the model before making predictions? Could the trained model be provided to make the developed tool easier to use?The user has to train the model again by himself or herself, because the model is larger than 1.7 GB as a file and therefore cannot be offered for download without paid services. Even with the best possible bzip2 compression, the file is still over 250 MB in size and thus larger than the 100 MB limit at GitHub. In addition, if the model is provided as a file, you must use exactly the same versions of the Python libraries used for training to load the model, otherwise inconsistencies may occur. However, if the model is trained again by yourself, only the minimum library requirements have to be met.
In the discussion section, the developed method should be first discussed before discussing its performance relative to other methods.This was done and is reflected in the new manuscript version.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. Manallack DT: The PK_a Distribution of Drugs: Application to Drug Discovery. Perspect Medicin Chem. 2007; 1: 25–38. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Manallack DT, Yuriev E, Chalmers DK: The influence and manipulation of acid/base properties in drug discovery. Drug Discov Today Technol. 2018; 27: 41–47. PubMed Abstract | Publisher Full Text

[3] 3. Manallack DT, Prankerd RJ, Yuriev E, et al.: The significance of acid/base properties in drug discovery. Chem Soc Rev. 2013; 42(2): 485–496. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Meanwell NA: Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol. 2011; 24(9): 1420–1456. PubMed Abstract | Publisher Full Text

[5] 5. Gleeson MP: Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem. 2008; 51(4): 817–834. PubMed Abstract | Publisher Full Text

[6] 6. Leeson PD, St-Gallay SA, Wenlock MC: Impact of Ion Class and Time on Oral Drug Molecular Properties. Med Chem Commun. 2011; 2(2): 91–105. Publisher Full Text

[7] 7. Charifson PS, Walters WP: Acidic and basic drugs in medicinal chemistry: a perspective. J Med Chem. 2014; 57(23): 9701–9717. PubMed Abstract | Publisher Full Text

[8] 8. Milletti F, Storchi L, Sforna G, et al.: New and original pK_a prediction method using grid molecular interaction fields. J Chem Inf Model. 2007; 47(6): 2172–2181. PubMed Abstract | Publisher Full Text

[9] 9. ACD/Percepta, Advanced Chemistry Development, Inc.. Toronto, On Canada, Www.Acdlabs.Com, 2019. Reference Source

[10] 10. Marvin 20.1.0, 2020. ChemAxon. Reference Source

[11] 11. Shelley JC, Cholleti A, Frye LL, et al.: Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des. 2007; 21(12): 681–691. PubMed Abstract | Publisher Full Text

[12] 12. Bochevarov AD, Watson MA, Greenwood JR, et al.: Multiconformation, Density Functional Theory-Based pK_a Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups. J Chem Theory Comput. 2016; 12(12): 6001–6019. PubMed Abstract | Publisher Full Text

[13] 13. Fraczkiewicz R, Lobell M, Göller AH, et al.: Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pK_a prediction. J Chem Inf Model. 2015; 55(2): 389–397. PubMed Abstract | Publisher Full Text

[14] 14. Roszak R, Beker W, Molga K, et al.: Rapid and Accurate Prediction of pK_a Values of C-H Acids Using Graph Convolutional Neural Networks. J Am Chem Soc. 2019; 141(43): 17142–17149. PubMed Abstract | Publisher Full Text

[15] 15. Mansouri K, Cariello NF, Korotcov A, et al.: Open-Source QSAR Models for PKa Prediction Using Multiple Machine Learning Approaches. J Cheminform. 2019; 11(1): 60. Publisher Full Text | Free Full Text

[16] 16. Sander T, Freyss J, von Korff M, et al.: DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015; 55(2): 460–473. PubMed Abstract | Publisher Full Text

[17] 17. Lewis RA, Rodde S: Novartis Pharma AG. Basel, Switzerland.

[18] 18. Settimo L, Bellman K, Knegtel RM: Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm Res. 2014; 31(4): 1082–1095. PubMed Abstract | Publisher Full Text

[19] 19. Liao C, Nicklaus MC: Comparison of nine programs predicting pK_a values of pharmaceutical substances. J Chem Inf Model. 2009; 49(12): 2801–2812. PubMed Abstract | Publisher Full Text

[20] 20. Avdeef A: Absorption and Drug Development: Solubility, Permeability, and Charge State. John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2012. Publisher Full Text

[21] 21. Morgenthaler M, Schweizer E, Hoffmann-Röder A, et al.: Predicting and tuning physicochemical properties in lead optimization: amine basicities. ChemMedChem. 2007; 2(8): 1100–1115. PubMed Abstract | Publisher Full Text

[22] 22. Luan F, Ma W, Zhang H, et al.: Prediction of pK_a for neutral and basic drugs based on radial basis function Neural networks and the heuristic method. Pharm Res. 2005; 22(9): 1454–1460. PubMed Abstract | Publisher Full Text

[23] 23. Dardonville C: Automated techniques in pK_a determination: low medium and high-throughput screening methods. Drug Discov Today Technol. 2018; 27: 49–58. PubMed Abstract | Publisher Full Text

[24] 24. Reijenga J, van Hoof A, van Loon A, et al.: Development of Methods for the Determination of pK_a Values. Anal Chem Insights. 2013; 8(1): 53–71. PubMed Abstract | Publisher Full Text | Free Full Text

[25] 25. RDKit, Open-Source Chemiformatics. Reference Source

[26] 26. QUACPAC 2.0.2.2: OpenEye Scientific Software, Santa Fe, NM. Reference Source

[27] 27. Pedregosa F, Weiss R, Brucher M: Scikit-Learn: Machine Learning in Python. J Mach Learn Res. 2011; 12: 2825–2830. Reference Source

[28] 28. Chen T, Guestrin C: XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’ 16; ACM Press: New York, New York, USA. 2016; 785–794. Publisher Full Text

[29] 29. Baltruschat M, czodrowskilab, Czodrowski P: czodrowskilab/Machine-learning-meets-pKa article (Version article). Zenodo. 2020. http://www.doi.org/10.5281/zenodo.3662245

[30] 30. Gaulton A, Hersey A, Nowotka M, et al.: The ChEMBL database in 2017. Nucleic Acids Res. 2017; 45(D1): D945–D954. PubMed Abstract | Publisher Full Text | Free Full Text

Machine learning meets pKa

Abstract

Keywords

Introduction

Methods

Different experimental methods

Figure 1. Correlation of Novartis compounds measured in potentiometric and high-throughput (capillary electrophoresis) set-up.

Figure 2. Intersection between ChEMBL and DataWarrior data sets.

Data set preparation

Figure 3. Distribution of the individual pKa values.

Learning

Implementation

Operation

Results

Table 1. Statistics of the five-fold cross-validation of the machine learning models.

Table 2. Predictive performance of the machine learning models the on the two external test sets.

Discussion and conclusions

Data availability

Source data

Software availability

Acknowledgments

References

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Machine learning meets pK_a

Figure 3. Distribution of the individual pK_a values.