DiffInt: A Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance

被引:1
作者
Sako, Masami [1 ]
Yasuo, Nobuaki [2 ]
Sekijima, Masakazu [1 ]
机构
[1] Inst Sci Tokyo, Dept Comp Sci, Yokohama, Kanagawa 2268501, Japan
[2] Tokyo Inst Technol, Acad Convergence Mat & Informat TAC MI, Meguro Ku, Tokyo 1528550, Japan
基金
日本学术振兴会;
关键词
KNOWLEDGE-BASED APPROACH; INFORMATION-SYSTEM; PROTEIN STRUCTURES; PREDICTION; DISCOVERY; DATABASE; DOCKING; PHARMACOPHORES; GLIDE;
D O I
10.1021/acs.jcim.4c01385
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
The design of drug molecules is a critical stage in the drug discovery process. The structure-based drug design has long played an important role in efficient development. Significant progress has been made in recent years in the generation of 3D molecules via deep generation models. However, while many existing models have succeeded in incorporating structural information on target proteins, they have not been able to address important interactions between protein and drug molecules, especially hydrogen bonds. In this study, we propose DiffInt as a novel structure-based approach that explicitly addresses interactions. The model naturally incorporates hydrogen bonds between protein and ligand molecules by treating them as pseudoparticles. The experimental results show that DiffInt reproduces hydrogen bonds, and the hydrogen binding energies significantly outperform those of existing models. To facilitate the use of our tool for generating new drug molecules based on any protein's three-dimensional structure, we have made the source code and trained model available on GitHub (https://github.com/sekijima-lab/DiffInt) under the MIT license, with the execution environment provided on Google Colab.
引用
收藏
页码:71 / 82
页数:12
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