Automated discovery of GPCR bioactive ligands

Abstract

While G-protein coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Due to the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structureactivity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.

G protein-coupled receptors: structure- and function-based drug discovery

“…243 Obviously, aided by >400 structures from 82 receptors, SBDD is now entering into a new era with substantial knowledge of GPCR signaling 103,110,244 and drug candidate attributes. 245…”

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

“…3D pharmacophore and ML methods [136][137][138] are widely used in the GPCR drug discovery community. In this review we have shown that 3D pharmacophores can be utilized at different stages of the drug discovery pipeline for GPCRs, e.g., in SBDD and LBDD strategies as a VS method (Section 2), guiding the selection of plausible molecular docking results, or in SAR studies to highlight interaction patterns important for affinity to the target.…”

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

“…Since GPCRs are involved in many different cellular and biological processes, make excellent drug targets, and remain orphaned to a relatively large extent, the prediction and consequent identification of GPCR ligands is an active area of research and interest [ 6 ]. Recent studies suggest that drugs target only approximately 10% of known GPCRs [ 7 ].…”