敏感:对小分子与上下文解释网络的结合有可解释的洞察力
Roshni Bhatt1,2, David Ryan Koes1, Jacob D Durrant2
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.
Journal of chemical information and modeling
|June 7, 2024
概括
我们开发了CENsible,这是一种可解释的深度学习方法,用于预测小分子结合亲和力. 这种方法有助于确定影响结合的关键因素,帮助药物发现和优化.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 机器学习在药物发现中的作用
背景情况:
- 准确预测小分子结合亲和力对于药物发现至关重要.
- 现有的机器学习模型往往缺乏可解释性,阻碍了优化工作.
研究的目的:
- 介绍CENsible,一种新且可解释的深度学习方法,用于评估蛋白质/配体结合亲和力.
- 为了证明CENsible能够区分活性与非活性化合物的能力.
主要方法:
- 使用上下文解释网络 (CEN),一个深层卷积神经网络架构.
- 预测预先计算的项对蛋白质/配体复合体的整体结合亲和力的贡献.
主要成果:
- 在各种系统中,CENsible有效地区分了活性与非活性化合物.
- 该模型通过确定每个术语对最终预测的贡献来提供可解释性.
结论:
- CENsible为现有的机器学习评分功能提供了一个可解释的替代方案.
- CENsible的可解释性对指导药物开发中的优化有直接影响.
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