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

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Masami Sako, Nobuaki Yasuo and Masakazu Sekijima*, 
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引用次数: 0

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.

基于结构的药物设计扩散模型与明确的氢键相互作用指导
药物分子的设计是药物发现过程中的关键阶段。长期以来,基于结构的药物设计在高效开发中发挥着重要作用。近年来,在通过深度生成模型生成三维分子方面取得了重大进展。然而,虽然许多现有的模型已经成功地结合了靶蛋白的结构信息,但它们还不能解决蛋白质和药物分子之间的重要相互作用,特别是氢键。在这项研究中,我们提出DiffInt作为一种新的基于结构的方法,明确地解决了相互作用。该模型将蛋白质和配体分子之间的氢键作为假粒子来处理。实验结果表明,DiffInt模型再现了氢键,并且氢结合能明显优于现有模型。为了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,我们在MIT许可下在GitHub (https://github.com/sekijima-lab/DiffInt)上提供了源代码和训练模型,执行环境在谷歌Colab上提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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