Peltason, Lisa Bertha: Systematic Computational Analysis of Structure-Activity Relationships. - Bonn, 2010. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
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author = {{Lisa Bertha Peltason}},
title = {Systematic Computational Analysis of Structure-Activity Relationships},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2010,
month = apr,

note = {The exploration of structure–activity relationships (SARs) of small bioactive molecules is a central task in medicinal chemistry. Typically, SARs are analyzed on a case-by-case basis for series of closely related molecules. Classical methods that explore SARs include quantitative SAR (QSAR) modeling and molecular similarity analysis. These methods conceptually rely on the similarity–property principle which states that similar molecules should also have similar biological activity. Although this principle is intuitive and supported by a wealth of observations, it is well-recognized that SARs can have fundamentally different character. Small chemical modifications of active molecules often dramatically alter biological responses, giving rise to “activity cliffs” and “discontinuous” SARs. By contrast, structurally diverse molecules can have similar activity, a situation that is indicative of “continuous” SARs. The combination of continuous and discontinuous components characterizes “heterogeneous” SARs, a phenotype that is frequently encountered in medicinal chemistry.
This thesis focuses on the systematic computational analysis of SARs present in sets of active molecules. Approaches to quantitatively describe, classify, and compare SARs at multiple levels of detail are introduced. Initially, a comparative study of crystallographic enzyme–inhibitor complexes is presented that relates two-dimensional and three-dimensional inhibitor similarity and potency to each other. The analysis reveals the presence of systematic and in part unexpected relationships between molecular similarity and potency and explains why apparently inconsistent SARs can coexist in compound activity classes. For the systematic characterization of complex SARs, a numerical function termed SAR Index (SARI) is developed that quantitatively describes continuous and discontinuous SAR components present in sets of active molecules. On the basis of two-dimensional molecular similarity and potency, SARI distinguishes between the three basic SAR categories described above. Heterogeneous SARs are further divided into two previously unobserved subtypes that are distinguished by the way they combine different SAR features. SARI profiling of various enzyme inhibitor classes demonstrates the prevalence of heterogeneous SARs for many classes. Furthermore, control calculations are conducted in order to assess the influence of molecular representation and data set size on SARI scoring. It is shown that SARI scores remain largely stable in response to variation of these critical parameters.
Based on the SARI formalism, a methodology is developed to study multiple global and local SAR components of compound activity classes. The approach combines graphical analysis of Network-like Similarity Graphs (NSGs) and SARI score calculations at multiple levels of detail. Compound classes of different global SAR character are found to produce distinct network topologies. Local SAR features are studied in subsets of similar compounds and systematically related to global SAR character. Furthermore, key compounds are identified that are major determinants of local and global SAR characteristics. The approach is also applied to study structure–selectivity relationships (SSRs). Compound selectivity often results from potency differences for multiple targets and presents a critical factor in lead optimization projects. Here, SSRs are explored for sets of compounds that are active against pairs of related targets. For this purpose, the molecular network approach is adapted to the evaluation of SSRs. Results show that SSRs can be quantitatively described and categorized in analogy to single-target SARs. In addition, local SSR environments are identified and compared to SAR features. Within these environments, key compounds are identified that determine characteristic features of single-target SARs and dual-target SSRs. Comparison of similar compounds that have significantly different selectivity reveals chemical modifications that render compounds target-selective.
Furthermore, a methodology is introduced to study SAR contributions from functional groups and substitution sites in series of analogous molecules. Analog series are systematically organized according to substitution sites in a hierarchical data structure termed Combinatorial Analog Graph (CAG), and the SARI scoring scheme is applied to evaluate SAR contributions of variable functional groups at specific substitution sites. Combinations of sites that determine SARs within analog series and make large contributions to SAR discontinuity are identified. These sites are prime targets for further chemical modification. In addition to determining key substitution patterns, CAG analysis also identifies substitution sites that have not been thoroughly explored.},

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