Drug targets prediction using chemical similarity

Abstract

The growing productivity gap between investment in drug research and development (R&D) and the number of new medicines approved by the US Food and Drug Administration (FDA) in the past decade is concerning. This productivity problem raises the need for innovative approaches for drug-target prediction and a deeper understanding of the interplay between drugs and their target proteins. Chemogenomics is the interdisciplinary field which aims to predict gene/protein/ligand relationships. The predictions are based on the assumption that chemically similar compounds should share common targets. Here, we exploit our understanding of the network-based representation of the protein-protein interaction (PPI network) to introduce a distance between drug-targets and could verify whether it correlates with their chemical similarity. We build a fully connected graph composed of US Food and Drug Administration (FDA) — approved drugs using the Tanimoto 2D similarity based on fingerprints from the SMILES representation of the chemical structure. Our analysis of 1165 FDA-approved drugs indicates that the chemical similarity of drugs predicts closeness of their targets in the human interactome.