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Updated: Jun 14, 2025

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一个人工智能加速虚拟选平台用于药物发现

Guangfeng Zhou^1,2, Domnita-Valeria Rusnac³, Hahnbeom Park^4,5

¹Department of Biochemistry, University of Washington, Seattle, WA, USA.

Nature communications

|September 5, 2024

概括

罗塞塔VS通过准确预测大型化合物库的分子相互作用来增强药物发现. 这种由人工智能驱动的平台快速识别出具有高结合性亲缘关系的有希望的药物线索.

科学领域:

计算化学和药物发现
结构生物学是结构生物学.
药理学中的人工智能

背景情况:

基于结构的虚拟查 (SBVS) 对于识别候选药物至关重要.
准确预测结合姿势和亲和力对于SBVS成功至关重要.
当前的方法面临着巨大的化学库和受体灵活性所带来的挑战.

研究的目的:

开发一种高度准确的SBVS方法,RosettaVS,用于预测对接姿势和结合亲和力.
为大规模虚拟选创建一个开源,AI加速的平台.
为了识别针对治疗点KLHDC2和Na_V的新型打击化合物1.7.7.

主要方法:

开发了RosettaVS,一种基于结构的先进虚拟选方法.
将受体灵活性建模纳入选方法.
利用人工智能加速平台对数十亿个复合库进行选,以对抗KLHDC2和NaV1.7.7.

主要成果:

罗塞塔VS在基准测试上表现优于最先进的方法.
确定了KLHDC2的7个命中化合物 (14%的命中率) 和NaV的4个命中率 (44%的命中率) 1.7.7.
所有已识别的热点均表现出单位的微分子结合亲缘关系,并且在不到七天内被发现.

结论:

由人工智能加速的RosettaVS平台能够快速准确地对庞大的复合库进行虚拟选.
该方法成功地确定了用于具有挑战性的目标的新化合物.
X射线结晶学验证了KLHDC2联体的预测结合姿势,证实了该方法在发现方面的有效性.

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