An up-to-date overview of computational polypharmacology in modern drug discovery.

ABSTRACT:

Introduction

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

Areas covered

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

SUBMITTER: Chaudhari R 

PROVIDER: S-EPMC7415563 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature