用图神经网络选药靶对接算法，又快又准。
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
- 用图神经网络分析蛋白-配体结构，预测各算法表现
- 在5300对复合物上验证，同时优化精度与计算效率
- 适合需要快速选最优对接算法的研究者
分子对接是药物发现的核心环节，通过模拟小分子与蛋白质的结合来预测相互作用。尽管存在多种对接算法，但无单一算法在所有场景下均表现最优。本文提出GNNAS-Dock，一种基于图神经网络（GNN）的自动化算法选择系统，用于盲对接场景。该系统利用GNN处理配体和蛋白质的复杂结构数据，借助其固有的图结构特性，预测不同条件下各类对接算法的性能。研究旨在实现两个目标：1）预测各候选算法的均方根偏差（RMSD），识别特定场景下的最准确方法；2）为每种对接任务选择计算效率最高的算法，在保证高精度的同时缩短耗时。方法在包含约5300对蛋白-配体复合物的PDBBind 2020精炼集上进行验证。
原文摘要 · Abstract (English)
Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins. Despite the availability of numerous docking algorithms, there is no single algorithm consistently outperforms the others across a diverse set of docking scenarios. This paper introduces GNNAS-Dock, a novel Graph Neural Network (GNN)-based automated algorithm selection system for molecular docking in blind docking situations. GNNs are accommodated to process the complex structural data of both ligands and proteins. They benefit from the inherent graph-like properties to predict the performance of various docking algorithms under different conditions. The present study pursues two main objectives: 1) predict the performance of each candidate docking algorithm, in terms of Root Mean Square Deviation (RMSD), thereby identifying the most accurate method for specific scenarios; and 2) choose the best computationally efficient docking algorithm for each docking case, aiming to reduce the time required for docking while maintaining high accuracy. We validate our approach on PDBBind 2020 refined set, which contains about 5,300 pairs of protein-ligand complexes.
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