Structure-based drug design with geometric deep learning
It addresses the problem of accelerating drug discovery for researchers and pharmaceutical companies by leveraging geometric deep learning, but it is incremental as it reviews existing applications rather than introducing new methods.
This review examines the application of geometric deep learning to structure-based drug design, highlighting its potential in tasks like molecular property prediction and de novo molecular design, though it does not report specific numerical results.
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.