Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-04 DOI:10.1109/JBHI.2024.3491318
Jia Zheng, Hai-Cheng Yi, Zhu-Hong You
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Abstract

De novo drug design speeds up drug discovery, mitigating its time and cost burdens with advanced computational methods. Previous work either insufficiently utilized the 3D geometric structure of the target proteins, or generated ligands in an order that was inconsistent with real physics. Here we propose an equivariant 3D-conditional diffusion model, named DiffFBDD, for generating new pharmaceutical compounds based on 3D geometric information of specific target protein pockets. DiffFBDD overcomes the underutilization of geometric information by integrating full atomic information of pockets to backbone atoms using an equivariant graph neural network. Moreover, we develop a diffusion approach to generate drugs by generating ligand fragments for specific protein pockets, which requires fewer computational resources and less generation time (65.98%  ∼  96.10% lower). DiffFBDD offers better performance than state-of-the-art models in generating ligands with strong binding affinity to specific protein pockets, while maintaining high validity, uniqueness, and novelty, with clear potential for exploring the drug-like chemical space. The source code of this study is freely available at https://github.com/haichengyi/DiffFBDD.

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用于新药设计的等变量三维条件扩散模型
新药设计加快了药物发现的速度,利用先进的计算方法减轻了时间和成本负担。以往的工作要么没有充分利用目标蛋白质的三维几何结构,要么生成的配体顺序与真实物理不符。在此,我们提出了一种等变三维条件扩散模型,名为 DiffFBDD,用于根据特定目标蛋白质口袋的三维几何信息生成新的药物化合物。DiffFBDD 利用等变图神经网络整合了口袋和骨干原子的全部原子信息,从而克服了几何信息利用不足的问题。此外,我们还开发了一种扩散方法,通过为特定蛋白质口袋生成配体片段来生成药物,这种方法所需的计算资源更少,生成时间更短(减少 65.98% ∼ 96.10%)。DiffFBDD 在生成与特定蛋白质口袋有强结合亲和力的配体方面比最先进的模型性能更好,同时还保持了较高的有效性、独特性和新颖性,在探索类药物化学空间方面具有明显的潜力。本研究的源代码可在 https://github.com/haichengyi/DiffFBDD 免费获取。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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