Structure-Promiscuity Relationship Puzzles-Extensively Assayed Analogs with Large Differences in Target Annotations

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Introducing the metacore concept for multi-target ligand design

In this work, we introduce the concept of "metacores" (MCs) for the organization of analog series (ASs) and multi-target (MT) ligand design. Generating compounds that are active against distantly related or unrelated targets is a central task in polypharmacology-oriented drug discovery. MCs are obtained by two-stage extraction of structural cores from ASs. The methodology is chemically intuitive and generally applicable. Each MC represents a set of related ASs and a template for the generation of new structures. We have systematically identified ASs that exclusively consisted of analogs with MT activity and determined their target profiles. From these ASs, a large set of 317 structurally diverse MCs was extracted, 127 of which were associated with different target families. The newly generated MCs were characterized and further prioritized on the basis of AS, compound, and target coverage. The analysis indicated that 260 MCs were pharmaceutically relevant. These MCs and the compound and target information they capture are made freely available for medicinal chemistry applications.

Prediction of Promiscuity Cliffs Using Machine Learning

Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identified. Nevertheless, the molecular origins of promiscuity remain to be elucidated. In this study, we systematically extracted different structural analogs with varying promiscuity using the matched molecular pair (MMP) formalism from public biological screening and medicinal chemistry data. Care was taken to eliminate all compounds with potential false-positive activity annotations from the analysis. Promiscuity predictions were then attempted at the level of compound pairs representing promiscuity cliffs (PCs; formed by analogs with large promiscuity differences) and corresponding non-PC MMPs (analog pairs without significant promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure-promiscuity relationship information than sets of individual promiscuous compounds. In addition, feature analysis was carried out revealing key contributions to the correct prediction of PCs and non-PC MMPs via machine learning.

Identifying Promiscuous Compounds with Activity against Different Target Classes

Compounds with multitarget activity are of high interest for polypharmacological drug discovery. Such promiscuous compounds might be active against closely related target proteins from the same family or against distantly related or unrelated targets. Compounds with activity against distinct targets are not only of interest for polypharmacology but also to better understand how small molecules might form specific interactions in different binding site environments. We have aimed to identify compounds with activity against drug targets from different classes. To these ends, a systematic analysis of public biological screening data was carried out. Care was taken to exclude compounds from further consideration that were prone to experimental artifacts and false positive activity readouts. Extensively assayed compounds were identified and found to contain molecules that were consistently inactive in all assays, active against a single target, or promiscuous. The latter included more than 1000 compounds that were active against 10 or more targets from different classes. These multiclass ligands were further analyzed and exemplary compounds were found in X-ray structures of complexes with distinct targets. Our collection of multiclass ligands should be of interest for pharmaceutical applications and further exploration of binding characteristics at the molecular level. Therefore, these highly promiscuous compounds are made publicly available.

Rationalizing Promiscuity Cliffs

Compound promiscuity can be viewed in different ways. We distinguish "bad" promiscuity resulting from chemical liabilities, nonspecific binding, or assay artifacts, from "good" promiscuity representing true multitarget activities. Investigating multitarget activities of small molecules is scientifically stimulating and therapeutically relevant. To better understand the molecular basis of multitarget activities, structure-promiscuity relationships (SPRs) are explored. For this purpose, "promiscuity cliffs" (PCs) have been introduced, which can be rationalized as an extension of the activity cliff (AC) concept. A PC is defined as a pair of structural analogues that are active against different numbers of targets (given a difference threshold). As discussed herein PCs frequently capture surprising SPRs and encode many experimentally testable hypotheses.