Comprehensive knowledge base of two- and three-dimensional activity cliffs for medicinal and computational chemistry

Introduction

The activity cliff concept has experienced increasing interest in chemical informatics and medicinal chemistry^1–5. A consensus definition of activity cliffs^1–4 refers to pairs or groups of structurally similar or analogous active compounds with large differences in potency^4,5. For the definition of activity cliffs, the specification of similarity and potency difference criteria is required. Two-dimensional activity cliffs (2D-cliffs) have mostly been defined on the basis of Tanimoto similarity⁶ comparing molecular fingerprint representations². More recently, 2D-cliffs have also been defined on the basis of substructure relationships, preferably employing the matched molecular pair (MMP) formalism^7,8, leading to the introduction of MMP-cliffs⁹. An MMP is defined as a pair of compounds that are only distinguished by a structural change at a single site⁷, i.e., the exchange of a substructure, termed a chemical transformation⁸. For the definition of MMP-cliffs, transformation size restrictions have been introduced to limit transformations to small chemical changes typically observed in analog series⁹. Applying well-defined similarity and potency difference criteria, 2D-cliffs can be systematically extracted from compound databases¹⁰.

The vast majority of 2D-cliffs (i.e., close to or more than 95%, depending on the molecular representations and similarity measure used) are not formed in isolation (i.e., in the absence of structural neighbors with significant potency variations), but rather in a coordinated manner involving series of compounds with varying potency forming multiple and overlapping cliffs^4,5,11. In activity cliff network representations where nodes represent compounds and edges activity cliffs, coordinated cliffs emerge as individual clusters of varying composition and size¹¹, which can be isolated for further analysis.

In addition to 2D-cliffs, three-dimensional activity cliffs (3D-cliffs) can also be defined by comparing compound binding modes in X-ray structures¹². This requires the superposition of structures of a given target available in different crystallographic ligand-target complexes and the assessment of the 3D similarity of bound ligands¹². Three-dimensional activity cliffs can be further extended by taking 2D ligand information into account. This can be accomplished by systematically searching compound activity classes for analogs of 3D-cliff partners¹³. For example, for each cliff partner, MMPs with database compounds sharing the same activity can be determined and qualifying analogs can be assigned to the 3D-cliff¹³, leading to what we term herein a 3D-cliff-MMP extension. Figure 1 shows an exemplary 2D-cliff (MMP-cliff), activity cliff cluster, 3D-cliff, and 3D-cliff-MMP extension.

Figure 1. Exemplary 2D- and 3D-cliffs.

Different categories of activity cliffs are shown formed by inhibitors of tyrosine kinase ABL. MMP-cliffs are used to represent 2D-cliffs. For each compound, the ChEMBL or Protein Data Bank (PDB) ID and its negative logarithmic potency value are reported. (a) An exemplary MMP-cliff (structural modification highlighted in red) taken from an activity cliff cluster (dashed blue box) is shown. In an activity cliff network, nodes represent compounds and edges cliffs. Nodes are colored according to potency values using a continuous color spectrum from red (lowest potency) via yellow (intermediate) to green (highest potency). In network representations, coordinated activity cliffs emerge as clusters. (b) An exemplary 3D-cliff and its 2D extension are shown (3D-cliff-MMP). The extension results from MMP-based mapping of analogs from ChEMBL to 3D-cliff compounds. Structural differences between 3D-cliff compounds and their 2D (MMP) partners are highlighted in red.

In medicinal chemistry, 2D-cliffs are often considered in the context of structure-activity relationship (SAR) analysis and compound design^2,3. For SAR exploration, activity cliff clusters are of particular interest because they provide more SAR information than 2D-cliffs studied individually. Furthermore, 3D-cliffs are of prime interest for structure-based design and also for computational chemistry applications including, for example, the calibration of scoring functions or free energy (perturbation) calculations. Last but not least, 2D extensions of 3D-cliffs bridge different applications in medicinal and computational chemistry and help to identify candidate compounds for further analysis.

Methods and materials

The activity cliff information provided herein is the result of recent surveys and systematic analyses of 2D-cliffs including clusters¹⁴, 3D-cliffs¹⁵, and extensions of 3D-cliffs¹⁶. Table 1 summarizes the different activity cliff categories. All 2D-cliffs reported herein originated from the most recent release of ChEMBL (version 20)^17,18 and all 3D-cliffs from the Protein Data Bank (PDB; accessed December, 2014)¹⁹. MMP extensions of 3D-cliffs were identified in ChEMBL (version 19).

For all activity cliffs, an at least 100-fold difference in potency between cliff partners was consistently required. For 2D-cliffs, only (assay-independent) K_i values were considered as potency measurements. For 3D-cliffs, K_i and IC₅₀ measurements were separately considered (using a K_i and IC₅₀ value-based data set, respectively). In addition, 3D-cliffs were also determined in a combined K_i/IC₅₀ data set (taking into consideration that 3D-cliffs provide a much smaller knowledge base than 2D-cliffs; vide infra). For 3D-cliff analysis, a crystallographic resolution limit of 3.0 Å was applied.

Two-dimensional activity cliffs were determined using three different molecular representations including the extended connectivity fingerprint with bond diameter 4 (ECFP4)²⁰, molecular access system (MACCS) structural keys²¹, and transformation size-restricted MMPs (MMP-cliffs)⁹. As similarity criteria for ECFP4- and MACCS-based activity cliffs, Tanimoto coefficient threshold values of 0.55 and 0.85 were applied, respectively^2,14. For an MMP-cliff, our preferred 2D-cliff definition^3,4, the formation of a transformation size-restricted MMP served as a similarity criterion. By definition 2D-cliffs do not contain stereochemical information.

For the identification of 3D-cliffs, the normalized overlap of atomic property density functions calculated for a pair of bound ligands was used as a measure of 3D similarity, taking conformational, positional, and atomic property differences into account¹². An at least 80% calculated 3D similarity was required as a threshold for 3D-cliff formation^12,15.

Data description

Activity cliff statistics are reported in Table 1. A total of 17,111 MMP-cliffs, 31,975 ECFP4-, and 34,813 MACCS-based 2D-cliffs were identified formed by compounds active against more than 300 targets in each case. The corresponding number of activity clusters (comprising at least 3 compounds) was 1267, 1462, and 1402, respectively. Therefore, a very large knowledge base of well-defined 2D-cliffs is currently available. In addition, on the basis of K_i and IC₅₀ measurements, 236 and 292 3D-cliffs were detected and were formed by crystallographic ligands of 26 and 43 targets, respectively. The combined K_i/IC₅₀ data set yielded 595 3D-cliffs for 58 targets. Although many more 2D- than 3D-cliffs are currently available, as one would expect, the number of 3D-cliffs is larger than we anticipated, hence providing substantial opportunities for structural and computational studies. Table 2 provides details for the 61 different targets for which 3D-cliffs were detected. Furthermore, more than 1000 3D-cliff-MMPs were identified for each of the K_i and IC₅₀ data sets and 2608 for the combined set (Table 1). Hence, for many 3D-cliffs, active analogs are available whose SAR characteristics and possible interaction patterns can be explored based upon 3D-cliff information, for example, by superposing them onto 3D-cliff compounds with which they form transformation size-restricted MMPs.

Data availability

The activity cliff information described above is made freely available in four separate data files containing 2D-cliffs, 3D-cliffs, 3D-cliff-MMP extensions, and superpositions of complex X-ray structures and 3D ligands for selected targets:

These data sets are contained in an open access ZENODO deposition²³. The deposition also contains a README document that details the data organization and information provided.

Conclusions

In this study, we have discussed different categories of activity cliffs (including cliff extensions) and reported the distribution of cliffs belonging to these categories. Given the cliff definitions applied herein, the activity cliff information we provide as an open access deposition is up-to-date and comprehensive. We hope that this large knowledge base of activity cliffs will be helpful in the practice of medicinal chemistry and structure-based drug design as well as in further evaluating and advancing computational methods.

Data availability

ZENODO: Knowledge base of two- and three-dimensional activity cliffs, doi: 10.5281/zenodo.18490²³