Structure-based drug design with geometric deep learning

TL;DR

This paper reviews how geometric deep learning, a neural network approach, is transforming structure-based drug design by improving molecular property prediction, ligand binding analysis, and de novo molecular generation.

Contribution

It provides a comprehensive overview of recent advances and future prospects of geometric deep learning in bioorganic and medicinal chemistry for drug discovery.

Findings

Enhanced molecular property prediction accuracy

Improved ligand binding site and pose prediction methods

Potential for de novo molecular design using geometric deep learning

Abstract

Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented.