评估神经网络中的点预测不确定性,以预测蛋白质-连接体结合
Ya Ju Fan1, Jonathan E Allen2, Kevin S McLoughlin2
1Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, USA.
Artificial intelligence chemistry
|August 16, 2023
概括
不确定性量化 (UQ) 对药物发现中的神经网络 (NN) 模型至关重要. 本研究探讨了UQ方法,以区分蛋白质 - 配体结合预测的不确定性来源,提高模型可靠性.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 机器学习用于分子建模.
背景情况:
- 神经网络 (NN) 模型加速药物发现,但需要不确定性量化 (UQ) 来探索新的化学空间.
- 标准的NN模型缺乏固有的不确定性估计,这限制了它们在预测培训数据之外的可靠性.
- 区分预测不确定性的来源对于药物发现中的知情决策至关重要.
研究的目的:
- 检查不确定性量化 (UQ) 方法,以估计NN模型中各种预测不确定性的来源,以预测蛋白质-连接体结合.
- 评估不同UQ方法对药物发现相关的各种不确定性类型的建模能力.
- 调查估计不确定性,预测错误和数据分区策略之间的关系.
主要方法:
- 利用先前的化学知识来设计与化合物的化学多样化分区的实验.
- 使用可视化方法创建不重叠的训练和测试集分割.
- 评估了在NN模型上选择的UQ方法,用于在不同的数据分区和特色化方案中预测蛋白质-配体结合.
主要成果:
- 证明选择的UQ方法可以区分各种不确定性来源.
- 展示了不确定性估计在不同的数据分区和特色化方法中如何变化.
- 建立了对NN模型的估计不确定性和预测错误之间的关系.
结论:
- 在药物发现中,UQ方法对于提高NN模型的可靠性至关重要.
- 区分不确定性来源的能力允许对模型预测进行更细致的解释.
- 这项工作为选择适当的UQ策略提供了对蛋白质 - 配体结合预测任务的洞察力.
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