Isert Clemens, Atz Kenneth, Schneider Gisbert
ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland.
ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland; ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 8093, Singapore.
Curr Opin Struct Biol. 2023 Apr;79:102548. doi: 10.1016/j.sbi.2023.102548. Epub 2023 Feb 24.
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.
基于结构的药物设计利用大分子（如蛋白质或核酸）的三维几何信息来识别合适的配体。几何深度学习是基于神经网络的机器学习中的一个新兴概念，已被应用于大分子结构。本文综述了几何深度学习在生物有机化学和药物化学中的最新应用，突出了其在基于结构的药物发现和设计中的潜力。重点在于分子性质预测、配体结合位点和构象预测以及基于结构的从头分子设计。文中强调了当前的挑战和机遇，并对几何深度学习在药物发现领域的未来进行了展望。
