Automated discovery of GPCR bioactive ligands

TL;DR

This paper reviews recent advances in using machine learning, especially deep learning, to automate the discovery of bioactive ligands for GPCRs, addressing challenges due to limited structural data.

Contribution

It provides a comprehensive overview of machine learning applications in GPCR ligand discovery and discusses future prospects with deep learning techniques.

Findings

Machine learning enhances bioactivity prediction for GPCR ligands.

Deep learning can extract salient features from structural data.

Automated methods accelerate ligand discovery processes.

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 structure-activity 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.