Structure-based drug design with geometric deep learning
By Clemens Isert, Kenneth Atz, Gisbert Schneider. 2022
178 citations recorded by the source corpus.
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.
Subfields: Graph Learning, Computational Chemistry & Drug Discovery
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In Graph Learning
In Computational Chemistry & Drug Discovery
Citations in the corpus
- Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, background): “Accurate models of biological complexes are critical to our understanding of cellular functions and for the rational design of therapeutics 2–4 , 9 .”
- Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, background): “Graph neural networks (GNNs) have seen broad applications in molecular feature extraction and property prediction 25–28 .”