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新型GPU引擎用于虚拟选千兆级图书馆,识别了挑战性目标的抑制剂

Eugene Raush¹, Maxim Totrov¹, Cody L Hoop²

¹Molsoft LLC, 11999 Sorrento Valley Road, San Diego, California 92121, United States.

Journal of chemical information and modeling

|September 17, 2025

概括

我们开发了两种GPU加速方法,RIDGE和RIDE,以加快虚拟连接体选和发现新的药物线索. 这些方法成功地确定了新型抑制剂,以挑战PD-L1和K-Ras G12D等癌症标.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

虚拟连接体查 (VLS) 对于从大型化学库中识别药物线索至关重要.
加速VLS对于高效的药物发现管道至关重要.
现有的方法可能会与庞大的数据集和具有挑战性的生物目标作斗争.

研究的目的:

开发和验证用于快速虚拟连接体选的GPU加速方法.
为了识别具有挑战性的癌症标 (PD-L1和K-Ras G12D) 的新型抑制剂.
为了提高对接分数的准确性,作为结合亲和力的预测指标.

主要方法:

开发了基于受体的快速对接GPU引擎 (RIDGE) 进行基于受体的选.
开发了基于联体的3D相似性选的快速异体发现引擎 (RIDE).
验证了RIDGE与来自DUD-E (Directory of Useful Decoys, Enhanced) 的102种蛋白质相对应.
使用RIDGE和RIDE对PD-L1和K-Ras G12D进行了超大虚拟库的选.

主要成果:

与现有方法相比,RIDGE表现出具有竞争力或优越的性能.
确定了五种PD-L1的新型抑制剂和三种K-Ras G12D的新型抑制剂,具有很高的亲和力.
来自RIDGE和RIDE的对接分数比传统VLS更好地预测结合.
成功选了超大型化学图书馆与具有挑战性的癌症标.

结论:

开发的GPU加速方法 (RIDGE和RIDE) 显著提高了虚拟连接体选效率.
这些方法可以发现强大的药物线索,即使是难以向的蛋白质.
进一步优化可以扩大向能力,包括以前无法向的蛋白质.

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