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DiffInt:一种具有明确氢键相互作用引导的基于结构的药物设计扩散模型。

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

作者信息

Sako Masami, Yasuo Nobuaki, Sekijima Masakazu

机构信息

Department of Computer Science, Institute of Science Tokyo, Yokohama, Kanagawa 226-8501, Japan.

Academy for Convergence of Materials and Informatics (TAC-MI), Institute of Science Tokyo, Meguro-ku, Tokyo 152-8550, Japan.

出版信息

J Chem Inf Model. 2025 Jan 13;65(1):71-82. doi: 10.1021/acs.jcim.4c01385. Epub 2024 Dec 19.

Abstract

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.

摘要

药物分子的设计是药物发现过程中的关键阶段。基于结构的药物设计长期以来在高效研发中发挥着重要作用。近年来,通过深度生成模型生成三维分子取得了重大进展。然而,尽管许多现有模型成功纳入了靶蛋白的结构信息,但它们未能解决蛋白质与药物分子之间的重要相互作用,尤其是氢键。在本研究中,我们提出DiffInt作为一种明确解决相互作用的新型基于结构的方法。该模型通过将蛋白质和配体分子之间的氢键视为伪粒子,自然地纳入了氢键。实验结果表明,DiffInt能够重现氢键,并且氢键结合能显著优于现有模型。为便于使用我们的工具基于任何蛋白质的三维结构生成新的药物分子,并在谷歌Colab上提供了执行环境以及遵循麻省理工学院许可在GitHub(https://github.com/sekijima-lab/DiffInt)上提供了源代码和训练模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfc3/11733934/3b3bbf03f3de/ci4c01385_0001.jpg

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