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3D-EDiffMG:以3D等效扩散驱动的分子生成来加速药物发现

Chao Xu¹, Runduo Liu², Yufen Yao²

¹Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China.

Journal of pharmaceutical analysis

|July 18, 2025

概括

这项研究引入了用于药物发现的3D等价扩散驱动分子生成 (3D-EDiffMG) 模型. 该模型增强了基于支架的分子优化,产生稳定和多样化的类似药物的分子.

科学领域:

计算化学和化学信息学
药物的发现和开发.
医学中的人工智能

背景情况:

化合物优化对于药物发现至关重要,需要提高生物活性和ADMET特性.
当前的深层分子生成模型与脚手架修改,远程依赖以及分子稳定性/多样性作斗争.
现有的扩散模型缺乏原子间约束特征,限制了它们在复杂分子结构中的应用.

研究的目的:

开发一种先进的深度分子扩散生成模型,用于基于支架的分子优化.
解决现有模型中关于原子间约束和远程依赖的局限性.
为了加速药物发现,产生新型,稳定和多样化的类似药物的分子.

主要方法:

提出了3D等效扩散驱动分子生成 (3D-EDiffMG) 模型.
引入了基于双强和弱原子相互作用力的长距离依赖,捕获等价编码器 (双SWLEE) 进行全面的原子相互作用编码.
整合了一个门多层感知子 (gMLP) 块,对模拟复杂的特征相互作用和远程依赖关系给予了微小的关注.

主要成果:

3D-EDiffMG有效地产生独特,新,稳定和多样化的类似药物的分子.
与现有方法相比,该模型在捕捉远程依赖和原子间约束方面表现出卓越的性能.
实验结果验证了模型对基于支架的分子优化能力.

结论:

3D-EDiffMG提供了一种有前途的方法,可以加速药物发现中的优化.
该模型产生稳定和多样化的分子的能力提高了其创造新疗法的潜力.
这项工作推进了深度生成模型在计算药物设计中的应用.

关键词:

深度分子扩散生成模型药物发现药物发现这是一个双等价编码器.领导结构优化优化领导结构优化分子产生产生分子.

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