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DSDP:一个盲目对接策略,由GPU加速

YuPeng Huang¹, Hong Zhang¹, Siyuan Jiang¹

¹College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.

Journal of chemical information and modeling

|June 30, 2023

概括

深部站点和对接姿势 (DSDP) 通过结合传统和机器学习方法来增强药物发现的盲目对接. 这种方法显著提高了识别潜在候选药物的准确性和速度.

科学领域:

计算化学是一种计算化学.
药物的发现和开发.
生物信息学是一种生物信息学.

背景情况:

虚拟查,特别是分子对接,对于识别候选药物至关重要.
传统的对接方法耗时,难以实现盲目对接的准确性.
机器学习对接方法提供速度,但往往缺乏足够的准确性.

研究的目的:

开发一种新的方法,Deep Site and Docking Pose (DSDP),它结合了传统和机器学习技术.
为了提高盲目对接在药物发现中的性能.
提高识别连接体结合点和位置的准确性和效率.

主要方法:

DSDP预测蛋白质结合部位,提供准确的搜索形状和初始连接体位置.
它使用了评分功能和修改的AutoDock Vina搜索策略.
使用GPU加速来加快构造性采样.

主要成果:

在1.2秒内,DSDP在一个具有挑战性的数据集 (RMSD < 2 Å) 上盲目对接中实现了29.8%的top-1成功率.
在DUD-E数据集中,DSDP显示了57.2%的top-1成功率,每系统0.8秒.
在PDBBind数据集上的性能在每系统1.0秒内产生了41.8%的top-1成功率.

结论:

在盲点对接的准确性和速度方面,DSDP显著超过了最先进的方法.
该方法有效地预测结合位点,并优化连接体位样本采集.
在药物发现中,DSDP代表了虚拟查的实质性进步.

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