A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction

doi:10.1038/s42004-025-01506-1

2025-04-07

Ming-Hsiu Wu

Ziqian Xie

Degui Zhi *

DOI：10.1038/s42004-025-01506-1

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摘要

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准确预测蛋白-配体结合亲和力在药物发现中至关重要。现有方法主要基于无对接技术，未明确考虑在缺乏结晶蛋白-配体结合构象场景下的原子级相互作用。如今，随着基于深度学习的AI蛋白折叠及结合构象预测技术的突破，我们能否改进结合亲和力预测？本研究引入了一个框架，即Folding-Docking-Affinity（FDA），该框架通过蛋白折叠、确定蛋白-配体结合构象，并基于三维蛋白-配体结合结构预测结合亲和力。我们的实验结果表明，FDA的性能与当前最先进的无对接方法相当。我们期望所提出的框架能为整合结合结构以实现更准确的结合亲和力预测提供一个起点。

Keyword:

GRAPH NEURAL-NETWORK

期刊

Communications Chemistry

IF:

6.2

论文数:

2.2K

被引数:

6.4K

机构

Univ Texas Hlth Sci Ctr Houston

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被引数: 98

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