SQM2.20:半经验性的量子力学评分函数在几分钟内产生了DFT质量的蛋白质-连接物结合亲缘关系预测
Adam Pecina1, Jindřich Fanfrlík1, Martin Lepšík1
1Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
Nature communications
|February 6, 2024
概括
我们开发了SQM2.20,这是一个快速的,基于物理学的评分函数,用于预测蛋白质 - 连接体结合亲和力. 该工具通过提供与昂贵方法相匹配的准确性来帮助计算机辅助药物设计,使其成为药物发现的实用工具.
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:
- 准确的蛋白质 - 配体结合亲和度估计对于计算机辅助药物设计至关重要.
- 现有的方法可能在计算上昂贵或缺乏准确性.
研究的目的:
- 推出SQM2.20,一个新的,通用的,基于物理的评分功能.
- 提供一个计算效率高,但准确的工具,用于绑定亲和力预测.
主要方法:
- 利用半经验量子力学方法来建模具有约束力的自由能量项.
- 开发了SQM2.20评分功能,结合了最近的进展.
- 编制了PL-REX基准数据集,具有高分辨率结构和实验亲和力.
主要成果:
- 与其他评分方法相比,SQM2.20显示出更高的性能.
- 与PL-REX数据集上的实验数据取得了很好的相关性 (平均R2 = 0.69).
- 显示的准确性与计算密集型DFT计算相比,但运行时间显著减少.
结论:
- SQM2.20为结合亲和力预测提供了一个计算效率高,准确的解决方案.
- 它的速度使其适合于在击中识别和领先优化方面的实际应用.
- PL-REX数据集是验证分数函数的宝贵资源.
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