Computational Tools for Polypharmacology and Repurposing

Abstract

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.

A Potential Target of Tanshinone IIA for Acute Promyelocytic Leukemia Revealed by Inverse Docking and Drug Repurposing

“…In silico methodologies have advanced as a valuable technique in early drug discovery and as more and more target structures, structure bioactivity data and, therefore, optimized chemoinformatic tools come available they are likely to expand impact within drug development (Achenbach et al, 2011). Inverse docking, first proposed in 2001, refers to computationally docking a specific small molecule of interest to a database of protein structure (Chen et al, 2001;Chen et al, 2014).…”

“…Thus, these results indicate that Tanshinone IIA may play a pharmacological role on RARα selectivity via these key residues. Drug repurposing is being used to systematically identify novel indications for drugs already known or discontinued in clinical development in recent years (Achenbach et al, 2011). CPI, an approach to drug repurposing, is an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc (Yang et al, 2011).…”

Active Learning for Computational Chemogenomics

“…Conversely, ligand-based prediction methods rely on mathematical representation of ligand structures and comparisons guided by the chemical similarity principle (structurally similar ligands often exhibit similar bioactivity) [11][12][13][14][15]. The choice for either approach is strongly governed by data availability or simply personal preference, with no clear winner among the numerous retrospective comparisons or when reviewing the literature on prospective applications [16][17][18].…”

Small Random Forest Models for Effective Chemogenomic Active Learning

“…molecules, targets, or interactions) [3,4]. Traditionally, computational studies that explore SARs either focus on leveraging molecular information about small molecules (ligand-based approaches) [5][6][7][8][9] or protein structures (receptor-based approaches) [10,11]. By combining these two worlds, chemogenomic (or proteochemometric) methods explore interaction spaces based on joint compound-protein descriptors and extrapolate on both target and chemical spaces while extending the model's applicability domain [12][13][14][15][16][17].…”