Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling.

Epigenetic drug discovery is an emerging strategy against several chronic and complex diseases. The increased interest in epigenetics has boosted the development and maintenance of large information on structure-epigenetic activity relationships for several epigenetic targets. In turn, such large databases-many in the public domain-are a rich source of information to explore their structure-activity relationships (SARs). Herein, we conducted a large-scale analysis of the SAR of epigenetic targets using the concept of activity landscape modeling. A comprehensive quantitative analysis and a novel visual representation of the epigenetic activity landscape enabled the rapid identification of regions of targets with continuous and discontinuous SAR. This information led to the identification of epigenetic targets for which it is anticipated an easier or a more difficult drug-discovery program using conventional hit-to-lead approaches. The insights of this work also enabled the identification of specific structural changes associated with a large shift in biological activity. To the best of our knowledge, this work represents the largest comprehensive SAR analysis of several epigenetic targets and contributes to the better understanding of the epigenetic activity landscape.

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes.

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.

Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps.

Structure-activity relationships (SAR) of compound databases play a key role in hit identification and lead optimization. In particular, activity cliffs, defined as a pair of structurally similar molecules that present large changes in potency, provide valuable SAR information. Herein, we introduce the concept of activity cliff generator, defined as a molecular structure that has a high probability to form activity cliffs with molecules tested in the same biological assay. To illustrate this concept, we discuss a case study where Structure-Activity Similarity maps were used to systematically identify and analyze activity cliff generators present in a dataset of 168 compounds tested against three peroxisome-proliferator-activated receptor (PPAR) subtypes. Single-target and dual-target activity cliff generators for PPARα and δ were identified. In addition, docking calculations of compounds that were classified as cliff generators helped to suggest a hot spot in the target protein responsible of activity cliffs and to analyze its implication in ligand-enzyme interaction.