论文标题
基于结构的药物设计，几何深度学习
Structure-based drug design with geometric deep learning
论文作者
论文摘要
基于结构的药物设计使用大分子（例如蛋白质或核酸）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的新兴概念，已应用于大分子结构。这篇综述概述了几何深度学习在生物有机和药物化学中的最新应用，从而强调了其基于结构的药物发现和设计的潜力。重点放在分子特性预测，配体结合位点和姿势预测以及基于结构的从头分子设计上。当前的挑战和机遇得到了强调，并提出了对药物发现的几何深度学习未来的预测。
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.
