Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs

Activity cliffs are formed by pairs or groups of structurally similar compounds having large differences in potency and are focal points of structure-activity relationship (SAR) analysis. The choice of molecular representations is a critically important aspect of activity cliffs analysis. Thus far, activity cliffs have predominantly been defined on the basis of molecular graph or fingerprint representations. Herein we introduce 3D activity cliffs derived from comparisons of experimentally determined compound binding modes. The analysis of 3D activity cliffs is generally applicable to target proteins for which structures of multiple ligand complexes are available. For two popular targets, β-secretase 1 (BACE1) and factor Xa (FXa), public domain X-ray structures with bound inhibitors were collected. Crystallographic binding modes of inhibitors were systematically compared using a 3D similarity method taking conformational, positional, and atomic property differences into account. In addition, standard 2D similarity relationships were also determined. SAR information associated with individual compounds substantially changed when either bioactive conformations or 2D molecular graphs were used for similarity evaluation. 3D activity cliffs were identified for BACE1 and FXa inhibitor sets and systematically compared to 2D cliffs. It was found that less than 40% of 3D activity cliffs were conserved when 2D similarity was applied. The limited conservation of 3D and 2D cliffs provides further evidence for the strong molecule representation dependence of activity cliffs. Moreover, 3D cliffs represent a new class of activity cliffs that convey SAR information in ways that differ from graph-based similarity measures. In cases where sufficient structural information is available, the comparison of 3D and 2D cliffs is expected to aid in SAR analysis and mapping of critical binding determinants.     

Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure

Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.     

Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account

Activity cliffs are formed by pairs or groups of structurally similar compounds with significant differences in potency. They represent a prominent feature of activity landscapes of compound data sets and a primary source of structure–activity relationship (SAR) information. Thus far, activity cliffs have only been considered for active compounds, consistent with the principles of the activity landscape concept. However, from an SAR perspective, pairs formed by structurally similar active and inactive compounds should often also be informative. Therefore, we have extended the activity cliff concept to also take inactive compounds into consideration. As source of both confirmed active and inactive compounds, we have exclusively focused on PubChem confirmatory bioassays. Activity cliffs formed between pairs of active compounds (homogeneous pairs) and pairs of active and inactive compounds (heterogeneous pairs) were systematically analyzed on a per-assay basis, hence ensuring the currently highest possible degree of experimental consistency in activity measurement. Only very small numbers of large-magnitude activity cliffs formed between active compounds were detected in PubChem bioassays. However, when taking confirmed inactive compounds from confirmatory assays into account, the activity cliff frequency in assay data significantly increased, involving 11–15 % of all qualifying pairs of similar compounds, depending on the molecular representations that were used. Hence, these non-conventional activity cliffs provide an additional source of SAR information.     

Design of multitarget activity landscapes that capture hierarchical activity cliff distributions

An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.     

Structure--activity landscape index: identifying and quantifying activity cliffs

A new method for analyzing a structure-activity relationship is proposed. By use of a simple quantitative index, one can readily identify "structure-activity cliffs": pairs of molecules which are most similar but have the largest change in activity. We show how this provides a graphical representation of the entire SAR, in a way that allows the salient features of the SAR to be quickly grasped. In addition, the approach allows us view the SARs in a data set at different levels of detail. The method is tested on two data sets that highlight its ability to easily extract SAR information. Finally, we demonstrate that this method is robust using a variety of computational control experiments and discuss possible applications of this technique to QSAR model evaluation.     

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach

In this work, we analyze the structure–activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors.

     

From activity cliffs to activity ridges: informative data structures for SAR analysis

The extraction of SAR information from structurally diverse compound data sets is a challenging task. One of the focal points of systematic SAR analysis is the search for activity cliffs, that is, structurally similar compounds having large potency differences, from which SAR determinants can be deduced. The assessment of SAR information is usually based on pairwise similarity and potency comparisons of data set compounds. As a consequence, activity cliffs are mostly evaluated at a compound pair level. Here, we present an extension of the activity cliff concept by introducing "activity ridges" that are formed by overlapping "combinatorial" activity cliffs between participating compounds, giving rise to ridge-like structures in activity landscapes. Activity ridges are rich in SAR information. In a systematic analysis of 242 compound data sets, we have identified well-defined activity ridges in 71 different sets. In addition, an information-theoretic approach has been devised to characterize the structural composition of activity ridges. Taken together, our results show that activity ridges frequently occur in sets of active compounds and that different categories of ridges can be distinguished on the basis of their structural content. The computational identification of activity ridges provides access to compound subsets having high priority for SAR analysis.     

Design of an activity landscape view taking compound-based feature probabilities into account

Activity landscapes (ALs) of compound data sets are rationalized as graphical representations that integrate similarity and potency relationships between active compounds. ALs enable the visualization of structure–activity relationship (SAR) information and are thus computational tools of interest for medicinal chemistry. For AL generation, similarity and potency relationships are typically evaluated in a pairwise manner and major AL features are assessed at the level of compound pairs. In this study, we add a conditional probability formalism to AL design that makes it possible to quantify the probability of individual compounds to contribute to characteristic AL features. Making this information graphically accessible in a molecular network-based AL representation is shown to further increase AL information content and helps to quickly focus on SAR-informative compound subsets. This feature probability-based AL variant extends the current spectrum of AL representations for medicinal chemistry applications.     

Further development of reduced graphs for identifying bioactive compounds

Reduced graphs provide summary representations of chemical structures. Here, a variety of different types of reduced graphs are compared in similarity searches. The reduced graphs are found to give comparable performance to Daylight fingerprints in terms of the number of active compounds retrieved. However, no one type of reduced graph is found to be consistently superior across a variety of different data sets. Consequently, a representative set of reduced graphs was chosen and used together with Daylight fingerprints in data fusion experiments. The results show improved performance in 10 out of 11 data sets compared to using Daylight fingerprints alone. Finally, the potential of using reduced graphs to build SAR models is demonstrated using recursive partitioning. An SAR model consistent with a published model is found following just two splits in the decision tree.     

Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes

We systematically compare X-ray structures of inhibitor complexes of four well-known enzymes and correlate two- and three-dimensional (2D and 3D) similarity of inhibitors with their potency. The analysis reveals the presence of unexpected systematic relationships between molecular similarity and potency. These findings explain why apparently inconsistent structure-activity relationships (SARs) can coexist in different targets, and they have general implications for compound screening and optimization. The results suggest that (1) even for active sites with significant binding constraints, there is a high probability that structurally diverse ligands with similar activity can be identified, (2) different types of SARs are not mutually exclusive, and (3) the chemical nature of ligands is of comparable importance for SARs as the features of active sites. These insights aid in the understanding of target-specific SARs and their intrinsic degree of variability.     

Systematic identification and classification of three-dimensional activity cliffs

Activity cliffs were systematically extracted from public domain X-ray structures of targets for which complexes with multiple ligands were available, following the concept of three-dimensional (3D) cliffs. Binding modes of ligands with well-defined potency measurements were compared in a pairwise manner, and their 3D similarity was calculated using a previously reported property density function-based method taking conformational, positional, and chemical differences into account. Requiring the presence of at least 80% 3D similarity and a potency difference of at least 2 orders of magnitude as cliff criteria, a total of 216 well-defined 3D activity cliffs were detected in the Protein Data Bank (PDB). These 3D-cliffs involved a total of 269 ligands active against 38 different targets belonging to 17 protein families. For 255 of these compounds, binding modes were available at high crystallographic resolution. All 3D-cliffs were analyzed in detail and assigned to different categories on the basis of crystallographic interaction patterns. In many instances, differences in ligand-target interactions suggested plausible causes for origins of 3D-cliffs. In other cases, short-range interactions seen in X-ray structures were insufficient to deduce possible reasons for cliff formation. The 3D-cliffs described herein further advance the rationalization of activity cliffs at the level of ligand-target interactions and should also be useful for other applications such as the calibration of energy functions for structure-based design. The pool of identified activity cliffs is provided to enable subsequent structure-based analyses of cliffs.