ArtiDock:精确的机器学习方法对蛋白质-连接体对接进行优化,用于高通量虚拟选
Taras Voitsitskyi1,2, Ihor Koleiev1,2, Roman Stratiichuk1,3
1Receptor.AI Inc., 20-22 Wenlock Road, London N1 7GU, U.K.
Journal of chemical information and modeling
|January 30, 2026
概括
ArtiDock是一种新的机器学习 (ML) 对接方法,通过提高准确性和速度来增强药物发现. 它在虚拟查中表现优于传统方法,特别是对于具有挑战性的蛋白质标.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 经典的蛋白质 - 配体对接方法在准确性和性能方面已经停滞不前.
- 机器学习 (ML) 对接显示出希望,但在准确性,基准测试和有效性方面面临挑战.
- 高通量虚拟选 (HTVS) 需要高效准确的对接工具.
研究的目的:
- 介绍ArtiDock,一个基于ML的对接技术,优化为HTVS.
- 在现实的场景中开发一个强大的基准来评估对接性能.
- 将ArtiDock的准确性和速度与已建立的经典和基于AI的对接方法进行比较.
主要方法:
- 开发了ArtiDock,这是一个基于ML的蛋白质-配体对接算法.
- 创建了用于口袋特定刚性对接基准的PLINDER数据集.
- 通过使用PLINDER和PoseX基准对比AutoDock,Vina,Glide和AI对接方法来评估ArtiDock.
主要成果:
- 与传统的对接方法 (AutoDock,Vina,Glide) 相比,ArtiDock的准确度提高了29-38%.
- 与PoseX上的AI对接和共同折叠方法相比,实现了更高吞吐量的竞争性准确性.
- 在具有挑战性的情况下,ArtiDock表现出卓越的性能,包括未结合的蛋白质和具有离子/水的位点.
结论:
- 在基于ML的对接中,ArtiDock为药物发现提供了重大进步.
- 该方法为HTVS应用提供了有利的准确性与速度的权衡.
- ArtiDock是HTVS推的工具,特别是在复杂的对接场景中.
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