KGDiff: towards explainable target-aware molecule generation with knowledge guidance

计算机科学 水准点(测量) 领域(数学分析) 亲缘关系 领域知识 结合亲和力 人工智能 化学空间 化学 药物发现 数学 立体化学 受体 数学分析 地理 生物化学 大地测量学
作者
Qian Hao,Wenjing Huang,Shikui Tu,Lei Xu
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:25 (1)
标识
DOI:10.1093/bib/bbad435
摘要

Abstract Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein–ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.

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