端到端盲对接和虚拟查的规范化蛋白质-连接体距离概率得分
Song Xia1, Yaowen Gu1, Yingkai Zhang1,2,3
1Department of Chemistry, New York University, New York, New York 10003, United States.
Journal of chemical information and modeling
|January 17, 2025
概括
我们开发了一个深度学习评分函数,即正常化混合密度网络 (NMDN) 评分,以改善药物发现的分子对接. 这种方法提高了蛋白质 - 配体结合强度预测和虚拟选效率.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 分子对接对于基于结构的虚拟选至关重要.
- 扩散模型擅长盲目对接,但缺乏结合强度估计.
- 精确的虚拟查需要蛋白质-连接体评分功能.
研究的目的:
- 引入一个深度学习 (DL) 评分函数,即正常化混合密度网络 (NMDN) 评分.
- 开发一个端到端的盲码对接和虚拟选协议 (DiffDock-NMDN).
- 在虚拟查中改善姿势选择和结合亲和力预测.
主要方法:
- 开发了NMDN评分,一个DL模型学习蛋白质残留-连接体原子距离分布.
- 集成了一个交互模块,用于实验性的结合亲和力预测.
- 创建了DiffDock-NMDN协议,将扩散模型与NMDN得分结合起来.
主要成果:
- 根据NMDN的得分,在姿势选择和虚拟查方面表现强.
- 在LIT-PCBA数据集中,DiffDock-NMDN的平均丰富系数为4.96.
- 该协议在药物发现中表现出有效性,具有有限的结合剂信息.
结论:
- NMDN评分提供了卓越的姿势选择和虚拟选功能.
- DiffDock-NMDN是用于现实世界药物发现场景的有效协议.
- 这项工作为未来的研究提供了基准和强大的DL评分功能.
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