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使用对接的HIt发现 ENriched by GEnerative Modeling (HIDDEN GEM):一种新的计算工作流程,用于加速超大化学库的虚拟选

Konstantin I Popov¹, James Wellnitz¹, Travis Maxfield¹

¹UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.

Molecular informatics

|October 6, 2023

概括

隐藏的宝石 (Hit发现使用对接丰富的GEnerative建模) 通过整合机器学习和生成化学加速虚拟选. 这种方法有效地从大量的化学图书馆中识别出顶级药物候选者,使用最小的计算资源.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
化学信息学化学信息学

背景情况:

超大型化学图书馆的扩张挑战了传统的虚拟选方法.
对于庞大的数据集来说,分子对接是计算密集的.

研究的目的:

开发一种用于加速虚拟选的新型计算方法.
为了提高效率,并减少来自大型化学图书馆的成功发现的资源需求.

主要方法:

机器学习,生成化学和化学相似性搜索的整合.
一个工作流程,将初始选与有针对性的分子对接相结合.
使用HIDDEN GEM (使用Docking ENriched by GEnerative Modeling进行HIT发现) 的方法.

主要成果:

隐藏的宝石显著加快了虚拟选过程.
与现有的加速方法相比,实现了最高的丰富系数.
通过16个不同的蛋白质点和37亿个分子库,证明了高效率.

结论:

隐藏的宝石提供了一个计算效率高的解决方案,用于击中发现.
该方法非常通用,与各种对接软件兼容.
允许对具有有限计算资源的用户进行有效的选.

关键词:

生成式建模生成式建模高通量虚拟选高通量虚拟选分子对接分子对接相似性搜索搜索相似性搜索

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