Computational analysis of activity and selectivity cliffs.

The exploration of structure-activity relationships (SARs) is a major challenge in medicinal chemistry and usually focuses on compound potency for individual targets. However, selectivity of small molecules that are active against related targets is another critical parameter in chemical lead optimization. Here, an integrative approach for the systematic analysis of SARs and structure-selectivity relationships (SSRs) of small molecules is presented.

Rationalizing three-dimensional activity landscapes and the influence of molecular representations on landscape topology and the formation of activity cliffs.

Activity landscapes are defined by potency and similarity distributions of active compounds and reflect the nature of structure-activity relationships (SARs). Three-dimensional (3D) activity landscapes are reminiscent of topographical maps and particularly intuitive representations of compound similarity and potency distributions. From their topologies, SAR characteristics can be deduced.

Computational analysis of multi-target structure-activity relationships to derive preference orders for chemical modifications toward target selectivity.

For series of compounds with activity against multiple targets, the resulting multi-target structure-activity relationships (mtSARs) are usually difficult to analyze. However, rationalizing mtSARs is of great importance for the development of compounds that are selective for one target over closely related ones. Herein we present a methodological framework for the study of mtSARs and identification of substitution sites in analogue series that are selectivity determinants.

Structural interpretation of activity cliffs revealed by systematic analysis of structure-activity relationships in analog series.

Discontinuity in structure-activity relationships (SARs) is caused by so-called activity cliffs and represents one of the major caveats in SAR modeling and lead optimization. At activity cliffs, small structural modifications of compounds lead to substantial differences in potency that are essentially unpredictable using quantitative structure-activity relationship (QSAR) methods. In order to better understand SAR discontinuity at the molecular level of detail, we have analyzed different compound series in combinatorial analog graphs and determined substitution patterns that introduce activity cliffs of varying magnitude.

From structure-activity to structure-selectivity relationships: quantitative assessment, selectivity cliffs, and key compounds.

The exploration of structure-activity relationships (SARs) in chemical lead optimization is mostly focused on activity against single targets. Because many active compounds have the potential to act against multiple targets, achieving a sufficient degree of target selectivity often becomes a major issue during optimization. Herein we report a data analysis approach to explore compound selectivity in a systematic and quantitative manner.

Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances.

The exploration of structure-activity relationships (SARs) of small molecules is a central aspect of medicinal chemistry. Typically, SARs are analyzed on a one-by-one basis, and chemical intuition and experience play an important role in this process. Since the 1960s, computational approaches have been developed to aid in SAR exploration that largely, but not exclusively, rely on the quantitative (Q)SAR paradigm.

Navigating structure-activity landscapes.

The problem of how to explore structure-activity relationships (SARs) systematically is still largely unsolved in medicinal chemistry. Recently, data analysis tools have been introduced to navigate activity landscapes and to assess SARs on a large scale. Initial investigations reveal a surprising heterogeneity among SARs and shed light on the relationship between 'global' and 'local' SAR features.

Exploration of structure-activity relationship determinants in analogue series.

A computational methodology is introduced to systematically organize compound analogue series according to substitution sites and identify combinations of sites that determine structure-activity relationships (SARs) and make large contributions to SAR discontinuity. These sites are prime targets for further chemical modification. The approach involves the analysis of substitution patterns in "combinatorial analogue graphs" (CAG) and the application of an SAR analysis function to evaluate contributions of variable R-groups.

Elucidation of structure-activity relationship pathways in biological screening data.

A computational molecular network analysis of various high-throughput screening (HTS) data sets including inhibition assays and cell-based screens organizes screening hits according to different local structure-activity relationships (SARs). The resulting network representations make it possible to focus on different local SAR environments in screening data. We have designed a simple scoring function accounting for similarity and potency relationships among hits that identifies SAR pathways leading from active compounds in different SAR contexts to key compounds forming activity cliffs.

Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices.

The study of structure-activity relationships (SARs) of small molecules is of fundamental importance in medicinal chemistry and drug design. Here, we introduce an approach that combines the analysis of similarity-based molecular networks and SAR index distributions to identify multiple SAR components present within sets of active compounds. Different compound classes produce molecular networks of distinct topology.

SAR index: quantifying the nature of structure-activity relationships.

Structure-activity relationships (SARs) can display very different features. Small chemical modifications of active molecules often dramatically alter biological responses. By contrast, structurally diverse molecules can have similar activity.

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.