Dilyana Dimova

Recent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry

The activity cliff concept is of high relevance for medicinal chemistry. Recent studies are discussed that have further refined our understanding of activity cliffs and suggested different ways of exploiting activity cliff information. These include alternative approaches to define and classify activity cliffs in two and three dimensions, data mining investigations to systematically detect all possible activity cliffs, the introduction of computational methods to predict activity cliffs, and studies designed to explore activity cliff progression in medicinal chemistry. The discussion of these studies is complemented with new findings revealing the frequency of activity cliff formation when different molecular representations are used and the distribution of activity cliffs across different targets. Taken together, the results have a number of implications for the practice of medicinal chemistry.

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.

Comprehensive Analysis of Single‐ and Multi‐Target Activity Cliffs Formed by Currently Available Bioactive Compounds

Activity cliffs are formed by structurally similar compounds having large potency differences. Their study is a focal point of SAR analysis. We present a first systematic survey of single‐ and multitarget activity cliffs contained in currently available bioactive compounds. Approximately 12% of all active compounds were involved in the formation of activity cliffs. Perhaps unexpectedly, activity cliffs were found to be similarly distributed over different protein target families. Moreover, only approximately 5% of all activity cliffs were multitarget cliffs. Importantly, we also found that only very few multitarget cliffs were formed by compounds having different target selectivity. In addition, ‘polypharmacological cliffs’, i.e., multitarget activity cliffs involving targets from different protein families, were also only rarely found. Taken together, our findings reveal that only approximately 2% of all pairs of structurally similar compounds sharing the same biological activity form activity cliffs but that, on average, approximately one of 10 active compounds is involved in the formation of one or two single‐target cliffs of large magnitude (with at least 100‐fold difference in potency). These compounds provide a rich source of SAR information and can be identified across many different target families.

Composition and topology of activity cliff clusters formed by bioactive compounds.

The assessment of activity cliffs has thus far mostly focused on compound pairs, although the majority of activity cliffs are not formed in isolation but in a coordinated manner involving multiple active compounds and cliffs. However, the composition of coordinated activity cliff configurations and their topologies are unknown. Therefore, we have identified all activity cliff configurations formed by currently available bioactive compounds and analyzed them in network representations where activity cliff configurations occur as clusters. The composition, topology, frequency of occurrence, and target distribution of activity cliff clusters have been determined. A limited number of large cliff clusters with unique topologies were identified that were centers of activity cliff formation. These clusters originated from a small number of target sets. However, most clusters were of small to moderate size. Three basic topologies were sufficient to describe recurrent activity cliff cluster motifs/topologies. For example, frequently occurring clusters with star topology determined the scale-free character of the global activity cliff network and represented a characteristic activity cliff configuration. Large clusters with complex topology were often found to contain different combinations of basic topologies. Our study provides a first view of activity cliff configurations formed by currently available bioactive compounds and of the recurrent topologies of activity cliff clusters. Activity cliff clusters of defined topology can be selected, and from compounds forming the clusters, SAR information can be obtained. The SAR information of activity cliff clusters sharing a/one specific activity and topology can be compared.

Design of multi-target 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. Furth...

Do Medicinal Chemists Learn from Activity Cliffs? A Systematic Evaluation of Cliff Progression in Evolving Compound Data Sets

Activity cliffs are defined as pairs of structurally similar compounds with a significant difference in potency. These compound pairs have high SAR information content because they represent small structural changes leading to large potency alterations. Accordingly, activity cliffs are of prime interest for SAR exploration and compound optimization. It is currently unknown to what extent activity cliff information is utilized in practical medicinal chemistry. Therefore, we have assembled 56 compound data sets that evolved over time and searched for analogues of activity cliff-forming compounds with further increased potency. For ∼75% of all activity cliffs, there was no evidence for further chemical exploration. For ∼25% of all cliffs, potency progression was detected. In total, for ∼15% of all activity cliffs, positive cliff progression was observed that often involved multiple analogues. Given these findings, chemically unexplored activity cliffs should provide significant opportunities for further study in medicinal chemistry.

Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles

A computational methodology is introduced for detecting all unique series of analogs in large compound data sets, regardless of chemical relationships between analogs. No prior knowledge of core structures or R-groups is required, which are automatically determined. The approach is based upon the generation of retrosynthetic matched molecular pairs and analog networks from which distinct series are isolated. The methodology was applied to systematically extract more than 17 000 distinct series from the ChEMBL database. For comparison, analog series were also isolated from screening compounds and drugs. Known biological activities were mapped to series from ChEMBL, and in more than 13 000 of these series, key compounds were identified that represented substitution sites of all analogs within a series and its complete activity profile. The analog series, key compounds, and activity profiles are made freely available as a resource for medicinal chemistry applications.

Assessing the Target Differentiation Potential of Imidazole-Based Protein Kinase Inhibitors

A library of 484 imidazole-based candidate inhibitors was tested against 24 protein kinases. The resulting activity data have been systematically analyzed to search for compounds that effectively differentiate between kinases. Six imidazole derivatives with high kinase differentiation potential were identified. Nearest neighbor analysis revealed the presence of close analogues with varying differentiation potential. Small structural modifications of active compounds were found to shift their inhibitory profiles toward kinases with different functions.

Advances in Computational Medicinal Chemistry: Matched Molecular Pair Analysis

Preclinical Research

Analog series-based scaffolds: computational design and exploration of a new type of molecular scaffolds for medicinal chemistry

Aim: Computational design of and systematic search for a new type of molecular scaffolds termed analog series-based scaffolds. Materials & methods: From currently available bioactive compounds, analog series were systematically extracted, key compounds identified and new scaffolds isolated from them. Results: Using our computational approach, more than 12,000 scaffolds were extracted from bioactive compounds. Conclusion: A new scaffold definition is introduced and a computational methodology developed to systematically identify such scaffolds, yielding a large freely available scaffold knowledge base.