整合多结构共价对接与机器学习共识评分,提高了人类乙胆酶抑制剂的功效排名
Chaitanya K Jaladanki1, Achal Ajeet Rayakar1, Yap Xiu Huan2
1Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis St, Matrix, Singapore 138671, Republic of Singapore.
Briefings in bioinformatics
|February 3, 2026
概括
这项研究引入了一种新的计算方法,结合了多结构共价对接和机器学习,以准确排名乙胆化酶 (AChE) 抑制剂的功效. 新方法提高了对共价抑制剂的预测,这对于治疗神经退行性疾病和毒性暴露至关重要.
科学领域:
- 计算化学是一种计算化学.
- 生物化学 生化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 乙胆酶 (AChE) 抑制对于治疗神经退行性疾病和抵消有毒暴露至关重要.
- 由于酶灵活性和多样化的化学结构,对共价ACHE抑制剂的排名具有挑战性.
- 现有的方法难以准确预测共价抑制剂的功效.
研究的目的:
- 开发一个改进的制方案,用于对共价ACHE抑制剂进行排名.
- 将多结构共价对接与机器学习共识得分集成.
- 为了提高基于对接的AChE抑制剂的强度预测的准确性.
主要方法:
- 分析了65个人类的ACHE晶体结构,以选择对接的代表性构造.
- 进行了412种有机酸盐和酸盐抑制剂的共价和非共价对接.
- 开发了一个机器学习共识模型,使用来自五个选定的结构的对接分数.
- 使用斯皮尔曼等级相关系数 (rs) 对实验逻辑IC50值进行评估预测.
主要成果:
- 协同对接显著优于非协同对接 (rs高达0.54vs0.18).
- 机器学习共识模型实现了最高的预测准确性 (rs = 0.70),超过了单一结构和启发式方法.
- 化学集群分析揭示了结构-活性关系.
- 沙普利添加式扩展证实了ML模型灵活整合结构数据的能力.
结论:
- 开发的in silico协议准确地排列了共价ACHE抑制剂的强度.
- 多结构共价对接与ML共识得分结合,提供了一个强大的策略.
- 这一框架可将其推广到其他联向蛋白质,以改善药物发现和毒性评估.
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