不确定性量化使得可靠的深度学习能够用于蛋白质-连接体结合亲和力预测
Milad Rayka1, S Shahab Naghavi2
1Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran, 1983969411, Iran. miladrayka93@gmail.com.
Scientific reports
|December 4, 2025
概括
本研究比较了深度学习模型的不确定性量化方法,这些模型预测了蛋白质-连接体结合亲和力. 使用前神经网络的Backprop的Bayes实现了药物发现的卓越性能和可靠的信心估计.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 深度学习 (DL) 模型对于在药物设计中预测蛋白质 - 配体结合亲和力至关重要.
- 当前的模型往往缺乏概括和信心估计,阻碍了可靠的决策.
研究的目的:
- 为了比较5种不确定性量化方法用于DL模型,预测蛋白质-连接体结合亲和力.
- 为此应用程序引入和评估Backprop的贝叶斯.
主要方法:
- 深层组合,蒙特卡洛脱落,拉普拉斯近似,贝叶斯通过背prop,和证据神经网络的比较.
- 使用防泄漏PDBBind数据集进行公正的评估.
- 使用推送神经网络 (FFNN) 与扩展连接互动功能 (ECIF).
主要成果:
- 与ECIF合作的FFNN和Backprop合作的Bayes显示出卓越的预测性能.
- 贝斯由Backprop提供了高度可靠的不确定性量化和优秀的校准.
- 这种方法在无需重新校准的情况下在多个评估指标中表现出色.
结论:
- 贝叶斯由Backprop是一个有前途的方法,用于不确定性量化在蛋白质-连接体结合亲和力预测.
- 这种方法提高了DL模型在药物发现中的可靠性和可重复性.
- 结果支持积极学习驱动的模型开发,以实现更强大的药物设计.
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