DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation

doi:10.1002/advs.202417726

2025-04-01

DOI：10.1002/advs.202417726

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摘要

En 中文

精确高效地设计具有多样化理化性质的潜在药物分子长期以来一直是一个关键挑战。近年来，各种基于深度学习的从头分子生成算法的出现为此问题提供了新的方向，其中去噪扩散模型已展现出显著潜力。然而，先前的方法往往难以同时优化候选化合物的多种性质，这可能源于直接采用非几何图神经网络（GNNs），导致其无法准确捕捉分子的拓扑和几何信息。本研究开发了一种双去噪扩散模型用于多条件分子生成（DiffMC-Gen），该模型整合了离散和连续特征以增强其感知3D分子结构的能力。此外，它引入了多目标优化策略，以同时优化目标分子的多种性质，包括结合亲和力、成药性、可合成性和毒性。从2D和3D分子生成的角度来看，DiffMC-Gen生成的分子在新颖性和独特性方面表现出当前最佳（SOTA）性能，同时在成药性和可合成性方面取得了与先前方法相当的结果。此外，生成的分子针对三种靶蛋白——LRRK2、HPK1和GLP-1受体——展现出良好的预测生物活性和成药性，同时保持了高标准的有效性、独特性和新颖性。这些结果突显了其在药物设计中的实际应用潜力。

Keyword:

deep learning

diffusion model

drug design

molecular generation

multi-objective optimization

期刊

Advanced Science

IF:

14.1

论文数:

1.8W

被引数:

11.5W

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