Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds

Cheminformatics Explorations of Natural Products

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications

For three promiscuity cliffs (enclosed), cliff compounds, their promiscuity degrees (PDs), and color-coded substitution sites are shown. Comparison of these cliffs suggests the design of a new analog to further explore promiscuity.

Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities

Structure-property relationships and structure-activity relationships play an important role in many research areas, such as medicinal chemistry and drug discovery. Such methods, however, have focused on providing post-hoc descriptions of such relationships based on known data. The ability for these descriptions to remain relevant when considering compounds of unknown activity, and thus the prediction of activity and property landscapes using existing data, remains little explored. In this study, we present a novel method of evaluating the ability of a compound comparison methodology to provide accurate information about a set of unknown compounds and also explore the ability of these predicted activity landscapes to prioritize active compounds over inactive. These methods are applied to three distinct and diverse sets of compounds, each with activity data for multiple targets, for a total of eight target-compound set pairs. Six methodologically distinct compound comparison methods were evaluated. We show that overall, all compound comparison methods provided an improvement in structure-activity relationship prediction over random and were able to prioritize compounds in a superior manner to random sampling, but the degree of success and therefore applicability varied markedly.

KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space

Protein kinases regulate the majority of signal transduction pathways in cells and have become important targets for the development of designer drugs. We present a systematic analysis of kinase-ligand interactions in all regions of the catalytic cleft of all 1252 human kinase-ligand cocrystal structures present in the Protein Data Bank (PDB). The kinase-ligand interaction fingerprints and structure database (KLIFS) contains a consistent alignment of 85 kinase ligand binding site residues that enables the identification of family specific interaction features and classification of ligands according to their binding modes. We illustrate how systematic mining of kinase-ligand interaction space gives new insights into how conserved and selective kinase interaction hot spots can accommodate the large diversity of chemical scaffolds in kinase ligands. These analyses lead to an improved understanding of the structural requirements of kinase binding that will be useful in ligand discovery and design studies.

Shifting from the single to the multitarget paradigm in drug discovery

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and high-throughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target-one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only 'key' compounds that fit with a single-target 'lock', but also 'master key' compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects).

Systematic characterization of structure-activity relationships and ADMET compliance: a case study

Traditionally, activity landscape modeling has been focused on analyzing SAR, despite the fact that lead optimization in drug discovery involves concurrent enhancements of activity and ADMET properties of leads. As a case study, we discuss the systematic analysis of activity landscapes, incorporating ADMET considerations, using a dataset of 166 compounds screened for kappa-opioid receptor activity. Pairwise MACCS/Tanimoto structure similarities, property similarities utilizing 33 ADMET descriptors and a 35-dimensional 'violation bit vector' representing drug-likeness are analyzed. We address the question about the range of ADMET property violations that arise from structural changes, subtle and significant. Pairs of compounds are identified bearing identical, comparable and significantly different drug-likeness in the three informative regions of structure-activity landscapes.

Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches

Systematic description of structure-activity relationships (SARs) of data sets and structure-property relationships (SPRs) is of paramount importance in medicinal chemistry and other research fields. To this end, structure-activity similarity (SAS) maps are one of the first tools proposed to describe SARs using the concept of activity landscape modeling. One of the major goals of the SAS maps is to identify activity cliffs defined as chemical compounds with high similar structure but unexpectedly very different biological activity. Since the first publication of the SAS maps more than ten years ago, these tools have evolved and adapted over the years to analyze various types of compound collections, including structural diverse and combinatorial sets with activity for one or multiple biological end points. The development of SAS maps has led to general concepts that are applicable to other activity landscape methods such as "consensus activity cliffs" (activity cliffs common to a series of representations or descriptors) and "selectivity switches" (structural changes that completely invert the selectivity pattern of similar compounds against two biological end points). Herein, we review the development, practical applications, limitations, and perspectives of the SAS and related maps which are intuitive and powerful informatics tools to computationally analyze SPRs.