使用多任务几何深度学习的蛋白质-配体复合物一步结构预测与筛选
One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning
AI总结：
针对药物开发中蛋白质-配体复合物结构预测与筛选的准确性及效率受限问题，提出基于多任务几何深度学习的单一模型LigPose，通过图表示直接优化三维结构并结合辅助任务，实现无需分子对接工具的一步预测，在主要药物研究任务上取得最先进性能。
摘要：
理解蛋白质-配体复合物的结构对药物开发至关重要。现有的虚拟结构测量和筛选方法主要由分子对接及其结合深度学习的衍生方法主导。然而，采样和打分方法在很大程度上限制了准确性和效率。在这里，我们展示了这两个基本任务可以通过基于多任务几何深度学习的单一模型（即LigPose）准确解决。通过将配体和蛋白质对表示为图，LigPose直接优化复合物的三维结构，并以结合强度和原子相互作用的学习作为辅助任务，使其无需分子对接工具即可实现一步预测能力。大量实验表明，LigPose在药物研究的主要任务上取得了最先进的性能。其显著的改进表明了一种有前景的基于AI的药物开发流程范式。
英文摘要：
Understanding the structure of the protein-ligand complex is crucial to drug development. Existing virtual structure measurement and screening methods are dominated by docking and its derived methods combined with deep learning. However, the sampling and scoring methodology have largely restricted the accuracy and efficiency. Here, we show that these two fundamental tasks can be accurately tackled with a single model, namely LigPose, based on multi-task geometric deep learning. By representing the ligand and the protein pair as a graph, LigPose directly optimizes the three-dimensional structure of the complex, with the learning of binding strength and atomic interactions as auxiliary tasks, enabling its one-step prediction ability without docking tools. Extensive experiments show LigPose achieved state-of-the-art performance on major tasks in drug research. Its considerable improvements indicate a promising paradigm of AI-based pipeline for drug development.
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