通过同源性预测亲和关系 (PATH):可解释的结合亲和关系预测与持久的同源性
Yuxi Long1, Bruce R Donald1,2
1Department of Computer Science, Department of Mathematics, Duke University, Durham, North Carolina, United States of America.
我们开发了PATH+,这是一种新的机器学习算法,用于准确的结合亲和力预测 (BAP),可以解释和通用. PATH+为药物设计提供了更好的速度和准确性,优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 结构生物学是结构生物学.
背景情况:
- 准确的结合亲和力预测 (BAP) 对于基于结构的药物设计至关重要.
- 当前的BAP算法往往缺乏可概括性和可解释性.
研究的目的:
- 为了介绍PATH+,BAP的新,可概括和可解释的机器学习算法.
- 为了提高药物发现的BAP的速度和准确性.
主要方法:
- 开发了PATH+,一种利用计算拓学的机器学习算法.
- 通过PATH+捕获的蛋白质-配体复合体的可视化特征.
- 创建了PATH- - 一个评分功能,用于绑定/非绑定区分.
主要成果:
- 与现有的BAP算法相比,PATH+在数据集中表现出类似或更高的准确性和通用性.
- PATH+ 是第一个内在可解释的 BAP 算法.
- PATH+的速度比主流的计算拓算法快10倍以上.
- PATH-在区分结合剂和非结合剂方面表现出卓越的准确性.
结论:
- PATH+为BAP提供了一种独特的解释性,速度和准确性的组合.
- 该算法能够对虚拟抑制器库进行拓选.
- 开源代码可用,促进对药物设计的信任和应用.
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