Bijun Zhang

Systematic assessment of compound series with SAR transfer potential.

Compound series with different core structures that contain pairs of analogs with corresponding substitution patterns and similar activity represent structure-activity relationship (SAR) transfer events. On the basis of the matched molecular pair (MMP) formalism and linear regression analysis of compound potencies, a general approach is introduced for the identification of SAR transfer series (SAR-TS) and SAR-TS with regular potency progression (SAR-TS-RP). We have systematically extracted such series from public domain compound data and analyzed their size distribution and structural characteristics. More than 900 SAR-TS and 500 SAR-TS-RP with high-confidence potency annotations were identified in various compound activity classes. These series provide a substantial knowledge base for the analysis and prediction of SAR transfer and are made publicly available.

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

AbstractChemical space networks (CSNs) have recently been introduced as an alternative to other coordinate-free and coordinate-based chemical space representations. In CSNs, nodes represent compounds and edges pairwise similarity relationships. In addition, nodes are annotated with compound property information such as biological activity. CSNs have been applied to view biologically relevant chemical space in comparison to random chemical space samples and found to display well-resolved topologies at low edge density levels. The way in which molecular similarity relationships are assessed is an important determinant of CSN topology. Previous CSN versions were based on numerical similarity functions or the assessment of substructure-based similarity. Herein, we report a new CSN design that is based upon combined numerical and substructure similarity evaluation. This has been facilitated by calculating numerical similarity values on the basis of maximum common substructures (MCSs) of compounds, leading to the introduction of MCS-based CSNs (MCS-CSNs). This CSN design combines advantages of continuous numerical similarity functions with a robust and chemically intuitive substructure-based assessment. Compared to earlier version of CSNs, MCS-CSNs are characterized by a further improved organization of local compound communities as exemplified by the delineation of drug-like subspaces in regions of biologically relevant chemical space.

Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity

AbstractChemical space networks (CSNs) have recently been introduced as a conceptual alternative to coordinate-based representations of chemical space. CSNs were initially designed as threshold networks using the Tanimoto coefficient as a continuous similarity measure. The analysis of CSNs generated from sets of bioactive compounds revealed that many statistical properties were strongly dependent on their edge density. While it was difficult to compare CSNs at pre-defined similarity threshold values, CSNs with constant edge density were directly comparable. In the current study, alternative CSN representations were constructed by applying the matched molecular pair (MMP) formalism as a substructure-based similarity criterion. For more than 150 compound activity classes, MMP-based CSNs (MMP-CSNs) were compared to corresponding threshold CSNs (THR-CSNs) at a constant edge density by applying different parameters from network science, measures of community structure distributions, and indicators of structure–activity relationship (SAR) information content. MMP-CSNs were found to be an attractive alternative to THR-CSNs, yielding low edge densities and well-resolved topologies. MMP-CSNs and corresponding THR-CSNs often had similar topology and closely corresponding community structures, although there was only limited overlap in similarity relationships. The homophily principle from network science was shown to affect MMP-CSNs and THR-CSNs in different ways, despite the presence of conserved topological features. Moreover, activity cliff distributions in alternative CSN designs markedly differed, which has important implications for SAR analysis.

AnalogExplorer: a new method for graphical analysis of analog series and associated structure-activity relationship information.

In recent years, several attempts have been made to develop graphical methods for the analysis of structure-activity relationships (SARs) in increasingly large and heterogeneous compound data sets. Among others, these approaches include extensions of conventional R-group tables and graph representations for the analysis of active analogs. Herein, we introduce AnalogExplorer as a new method for the graphical exploration of analog series. AnalogExplorer consists of three graphical components and is methodologically distinct from previous SAR visualization techniques. It is designed to deconvolute large series of analogs and systematically analyze and compare analog series contained in structurally heterogeneous data sets. In addition, analog subsets forming activity cliffs and R-groups responsible for cliff formation are easily identified in AnalogExplorer graphs. The design of AnalogExplorer is described in detail, and exemplary applications are discussed. In addition, the implementation of AnalogExplorer is made freely available.

SAR transfer across different targets.

Despite obvious relevance for the practice of medicinal chemistry, SAR transfer events have thus far only been little investigated in a systematic manner. Two types of SAR transfer can principally be distinguished. In target-based SAR (T_SAR) transfer, a series of corresponding analogs with different core structures display comparable potency progression against a given target. In addition, in series-based SAR (S_SAR) transfer, a given analog series shows comparable potency progression against two or more targets. Only a few studies have previously investigated T_SAR transfer. In these studies, T_SAR transfer series were frequently found for targets belonging to different families. By contrast, S_SAR transfer has thus far not been explored. It is currently unknown to what extent these S_SAR transfer events might occur in available compound data. We have devised an approach to detect S_SAR transfer and systematically searched public domain compound data for S_SAR transfer events. In total, 63 S_SAR transfer series involving two targets and 26 series involving three targets were identified. Series involving four targets were not found. The majority of S_SAR transfer series were identified for different subfamilies of G protein coupled receptors, but transfer series were also found for other target families. However, S_SAR transfer across different families was not observed. On average, S_SAR transfer series consisted of five to six analogs. The series were structurally diverse and represented SARs with varying degrees of continuity or discontinuity but displayed closely corresponding potency progression across related targets. All series and the corresponding source data sets are made freely available.

Method for Systematic Assessment of Chemical Changes in Molecular Scaffolds with Conserved Topology and Application to the Analysis of Scaffold-Activity Relationships.

Sets of scaffolds with conserved molecular topology are abundant among drugs and bioactive compounds. Core structure topology is one of the determinants of biological activity. Heteroatom replacements and/or bond order variation render topologically equivalent scaffolds chemically distinct and also contribute to differences in the biological activity of compounds containing these scaffolds. Relationships between core structure topology, chemical modifications, and observed activity profiles are difficult to analyze. A computational method is introduced to consistently assess chemical transformations that distinguish scaffolds with conserved topology. The methodology is applied to quantify chemical differences in conserved topological environments and systematically relate chemical changes in topologically equivalent scaffolds to associated activity profiles.

AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series

AnalogExplorer is a computational methodology for the extraction and organization of series of structural analogs from compound data sets and their graphical analysis. The method is suitable for the analysis of large analog series originating from lead optimization programs. Herein we report AnalogExplorer2 designed to explicitly take stereochemical information during graphical analysis into account and describe a freely available deposition of the original AnalogExplorer program, AnalogExplorer2, and exemplary compound sets to illustrate their use.

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.

Matched molecular pair-based data sets for computer-aided medicinal chemistry

Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The data sets have in common that they have been derived from compounds included in the ChEMBL database (release 17) for which high-confidence activity data are available. Thus, the activity data associated with MMP-based activity cliffs, SAR transfer series, and retrosynthetic MMPs cover the entire spectrum of current pharmaceutical targets. Our data sets are made freely available to the scientific community.