A structure-based framework for selective inhibitor design and optimization

Yurong Zou

doi:10.1038/s42003-025-07840-3

2025-03-12

DOI：10.1038/s42003-025-07840-3

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En 中文

基于结构的药物设计旨在通过分析靶点结构来创建具有优良性质的活性化合物。近年来，深度生成模型促进了结构特异性的分子生成。然而，许多方法受限于药物数据的不足，导致分子性质欠佳和构象不稳定。此外，这些方法常忽略结合口袋的相互作用，且难以进行选择性抑制剂设计。为解决这些挑战，我们开发了一个名为粗粒度与多维数据驱动的分子生成（CMD-GEN）的框架。CMD-GEN通过利用来自扩散模型的粗粒度药效基点，将配体-蛋白复合物与类药分子联系起来，以丰富训练数据。通过分层架构，它将口袋内的三维分子生成分解为药效基点采样、化学结构生成和构象对齐，从而缓解不稳定性问题。CMD-GEN在基准测试中优于其他方法，并能有效控制类药性。此外，CMD-GEN在三个合成致死靶点案例中表现优异，且PARP1/2抑制剂的湿实验验证证实了其在选择性抑制剂设计中的潜力。

Keyword:

DRUG DESIGN

CHALLENGES

KINASE

期刊

Communications Biology

IF:

5.1

论文数:

1.0W

被引数:

3.2W

机构

chengdu zenitar biomed technol co ltd

学者数:

论文数: 3

被引数: 0

Univ Melbourne

学者数:

3.4K

论文数: 1.9K

被引数: 694

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