Introduction
The compound data sets reported in our original article1 and the new data sets presented herein have resulted from research in the chemoinformatics and medicinal chemistry area and have mostly been generated from public domain repositories of compound structures and activity data. In addition, software tools made publicly available have also been developed in our laboratory1. Data sets reported in the scientific literature in the context of computational method development and evaluation are often not publicly available, which limits the reproducibility of computational investigations and comparisons of different computational methods. We believe that it is important to provide such data to the scientific community to further improve the transparency and credibility of computational studies and support method development. In addition to the data sets designed for the development and evaluation of computational methods, we also make available data sets that were generated as a resource and knowledge base for medicinal chemistry applications. Our data sets and tools are provided via the ZENODO platform (https://zenodo.org/) to ensure easy and stable access.
Materials and methods
The data sets reported herein were predominantly generated from ChEMBL2,3, BindingDB4 and PubChem5 (a few exceptions are specified in the original data article1). Compound structures are represented as SMILES6 strings or SD files7. Activity information and other (data set-dependent) annotations are provided in the individual data files. For software tools (written in different languages), the source code is also made available.
Data description
Table 1 provides the updated list and classification of all freely available data sets and programs. Entries were organized according to the following scientific subject areas: data sets for structure-activity relationship (SAR) and structure-selectivity relationship (SSR) analysis, SAR visualization (SAR_VZ), and virtual screening via similarity searching or machine learning (VS_ML). In addition, the programs are provided separately (PROG). Data sets and programs are contained in separate ZENODO deposition sets with a unique reference. Three matched molecular pair (MMP)-based data sets also included in our update have recently been reported and described in detail8. Entries 1–30 in Table 1 represent the data sets and programs that we initially provided via our website1 and entries 31–43 represent new data sets. In the following, the new data sets are described:
Table 1. Data sets and programs.
| Entry | Year | Subject area
index label | Description |
|---|
| 1[9] | 2007 | VS_ML_1 | 9 activity classes (AC) with increasing structural diversity |
| 2[9] | 2007 | VS_ML_2 | ~1.44 million ZINC compounds used for various virtual screening trials |
| 3[10] | 2007 | PROG_1 | Molecular similarity histogram filtering |
| 4[11] | 2007 | SSR_1 | 4 SD files with 26 selectivity sets; compounds are annotated with selectivity values for different targets |
| 5[12] | 2008 | SSR_2 | 7 compound selectivity sets containing 267 biogenic amine GPCR antagonists |
| 6[13] | 2008 | SSR_3 | 18 selectivity sets for targets from 4 families |
| 7[14] | 2008 | VS_ML_3 | 25 sets of compounds of increasing complexity and size |
| 8[15] | 2009 | VS_ML_4 | 242 hERG inhibitors |
| 9[16] | 2009 | SSR_4 | 243 ionotropic glutamate ion channel antagonists |
| 10[17] | 2009 | PROG_2 | Combinatorial analog graph (CAG) program with a sample set consisting of 51 thrombin inhibitors |
| 11[18] | 2009 | VS_ML_5 | 20 AC from the literature and 15 AC from the Molecular Drug Data Report |
| 12[19] | 2010 | VS_ML_6 | 8 AC |
| 13[20] | 2010 | PROG_3 | Program to generate target selectivity patterns of scaffolds |
| 14[21] | 2010 | PROG_4 | Multi-target CAGs (see also entry 10) with a sample set containing 33 kinase inhibitors |
| 15[22] | 2010 | PROG_5 | SARANEA |
| 16[23] | 2010 | PROG_6 | 3D activity landscape program with a sample set containing 248 cathepsin S inhibitors |
| 17[24] | 2010 | SAR_1 | 2 sets of MMPs from BindingDB and ChEMBL |
| 18[25] | 2010 | PROG_7 | Similarity-potency tree (SPT) program with a sample set containing 874 factor Xa inhibitors |
| 19[26] | 2010 | VS_ML_7 | 17 target-directed compound sets; each set contains a minimum of 10 distinct scaffolds and each
scaffold represents 5 compounds |
| 20[27] | 2011 | SAR_VZ | 10,489 malaria screening hits |
| 21[28] | 2011 | SAR_2 | 458 target-based sets with scaffolds and scaffold hierarchies |
| 22[29] | 2011 | SAR_VZ | 4 sets of compounds active against 3 or 4 targets |
| 23[30] | 2011 | SAR_VZ | 881 factor Xa inhibitors |
| 24[31] | 2011 | VS_ML_8 | 50 AC prioritized for similarity searching |
| 25[32] | 2011 | VS_ML_9 | 25 data sets from successful ligand-based virtual screening applications |
| 26[33] | 2011 | SAR_3 | 26 conserved scaffolds in activity profile sequences of length 4 |
| 27[34] | 2011 | PROG_8 | Scaffold distance function |
| 28[35] | 2011 | SAR_4 | 2 sets of compounds with multiple Ki or IC50 measurements against the same targets that differed within
1 order of magnitude |
| 29[36] | 2012 | SAR_VZ | 4 AC |
| 30[37] | 2012 | SAR_5 | 5 sets of different types of activity cliffs |
| 31[38] | 2012 | VS_ML_10 | 50 AC for scaffold hopping analysis |
| 32[39] | 2012 | SAR_6 | 61 AC consisting of SAR transfer series with regular potency progression |
| 33[40] | 2013 | SAR_7 | 4 activity measurement type-dependent sets of scaffolds |
| 34[41] | 2013 | VS_ML_11 | 2 multi-target compound sets |
| 35[42] | 2013 | VS_ML_12 | 4 multi-target compound sets and 3 multi-mechanism sets |
| 36[43] | 2013 | SAR_8 | 2337 compound series matrices |
| 37[44] | 2013 | SAR_9 | 128 AC containing ≥100 compounds with Ki values |
| 38[45] | 2014 | SAR_10 | 30,452 and 45,607 target-based MMS with Ki and IC50 values, respectively |
| 39[46] | 2014 | SAR_11 | 221 drug-unique scaffolds |
| 40[47] | 2014 | SAR_12 | 92,734 MMPs based upon retrosynthetic rules for 435 AC |
| 41[8] | 2014 | SAR_13 | 20,073 and 25,297 MMP-based activity cliffs with Ki and IC50 values, respectively |
| 42[8] | 2014 | SAR_14 | 4 activity measurement type-dependent sets of SAR transfer series with approximate or regular
potency progression |
| 43[8] | 2014 | SAR_15 | 169,889 and 240,322 transformation size-restricted MMPs based upon retrosynthetic rules with Ki and
IC50 values, respectively |
Entry 31
50 compound activity classes (AC) are prioritized for the evaluation of scaffold hopping potential in ligand-based virtual screening38. These AC contain the largest proportion of scaffold pairs with largest chemical inter-scaffold distances38 that can be derived from current bioactive compounds and hence present challenging test cases for scaffold hopping analysis.
Entry 32
596 SAR transfer series with regular potency progression (SAR-TS-RP) are extracted from 61 AC39. Each SAR-TS-RP represents two compound series with different core structures and pairwise corresponding substitutions that yield comparable potency progression against a given target. These series provide a knowledge base for the analysis and prediction of SAR transfer events.
Entry 33
Four sets of molecular scaffolds (with each scaffold representing more than ten compounds) are provided that are active against a single target (ST), multiple targets from the same family (SF), or multiple targets from different families (MF)40. Data sets are separately assembled for different types of potency measurements (i.e., Ki and IC50 values) and provide a resource of scaffolds representing compounds with varying degrees of target promiscuity.
Entry 34
Two multi-target compound data sets consist of confirmed screening hits41. Each set contains compounds with single-, dual-, and triple-target activity, or no activity. These data provide test cases for machine learning or other approaches to differentiate between compounds with overlapping yet distinct activity profiles.
Entry 35
Four multi-target compound data sets are provided42. Each set contains compounds tested in three different assays. Compounds are organized into eight different subsets according to their activity profiles, i.e., single-, dual-, and triple-target activity, or no activity. In addition, three multi-mechanism compound sets are designed42. In the latter case, compounds are organized into four subsets according to their mechanism-of-action. These data sets also represent test cases for machine learning to distinguish compounds with different activity profiles or mechanisms.
Entry 36
2337 non-redundant compound series matrices (CSMs) are generated covering compounds active against a wide spectrum of targets43. Each matrix contains at least two analogous matching molecular series (MMS) with structurally related yet distinct cores. A matrix consists of known active compounds and structurally related virtual compounds and hence provides suggestions for compound design.
Entry 37
128 target-based data sets are assembled that consist of at least 100 compounds with precisely specified equilibrium constants (Ki values) below 1 µM for human targets44. These high-confidence activity data sets provide a sound basis for SAR exploration.
Entry 38
30,452 and 45,607 target-based MMS with Ki and IC50 values, respectively, are extracted from bioactive compounds45.
Entry 39
221 scaffolds are identified that only occur in approved drugs but are not found in currently available bioactive compounds46. Accordingly, these scaffolds have been termed drug-unique scaffolds.
Entry 40
92,734 MMPs are generated from 435 AC on a basis of retrosynthetic rules47. These MMPs consider chemical reaction information and should be useful for practical medicinal chemistry applications.
Entry 41
20,073 and 25,297 MMP-based activity cliffs (i.e. pairs of structurally analogous compounds with an at least 100-fold difference in potency) are extracted from specifically active compounds based upon Ki and IC50 values, respectively8. The MMP-based activity cliffs provide a large knowledge base for SAR analysis.
Entry 42
157 and 513 MMP-based SAR transfer series with approximate potency progression plus 60 and 322 SAR transfer series with regular potency progression based upon Ki and IC50 values, respectively, are isolated from bioactive compounds. These transfer series are active against individual targets8. Similar to MMP-based activity cliffs, SAR transfer series provide a resource for SAR analysis and compound design.
Entry 43
169,889 and 240,322 transformation size-restricted MMPs based upon retrosynthetic rules with Ki and IC50 values, respectively, are systematically extracted from available AC8. Different from the retrosynthetic rule-based MMPs presented above, applied transformation size-restrictions ensure that chemical changes distinguishing compounds in pairs are small.
Summary
Herein we have provided an updated release of data sets and programs for chemoinformatics and medicinal chemistry that we make freely available. In total, 13 new data sets are introduced. Transferring all data entries in an organized form to the ZENODO platform makes them easily accessible. We hope that our current release might be of interest and helpful to many investigators in academia and the pharmaceutical industry.
Data availability
ZENODO: Programs for chemoinformatics and computational medicinal chemistry, doi: 10.5281/zenodo.845148.
ZENODO: Data sets for chemoinformatics and computational medicinal chemistry, doi: 10.5281/zenodo.845549.
Author contributions
JB designed the study, YH collected and organized the data, YH and JB wrote the manuscript.
Competing interests
No competing interests were declared.
Grant information
The author(s) declared that no grants were involved in supporting this work.
Acknowledgments
We are grateful to current and former members of our research group who have contributed to the development of the data sets and programs reported herein.
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