One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning
This addresses the accuracy and efficiency limitations in virtual structure measurement and screening for drug development, representing a novel paradigm rather than an incremental improvement.
The paper tackled the problem of predicting protein-ligand complex structures and screening, which is crucial for drug development, by introducing LigPose, a multi-task geometric deep learning model that directly optimizes 3D structures without docking tools, achieving state-of-the-art performance in major drug research tasks.
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.