使用后方网络减少分子性质分类中的过度自信错误
Zhehuan Fan1,2, Jie Yu1,2, Xiang Zhang3
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Patterns (New York, N.Y.)
|July 15, 2024
概括
这项研究通过将Softmax替换为正常化流量来增强药物开发中的分子性质预测. 这提高了不确定性估计,减少了对分布外样本的昂贵过度自信错误预测.
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:
- 深度学习模型对于预测药物开发中的分子特性至关重要.
- 传统的基于Softmax的模型表现出不良的不确定性估计,导致对分布外数据进行过度自信的错误预测.
- 这些限制在药物开发管道中带来了重大风险和成本.
研究的目的:
- 改进深度学习模型中的不确定性估计,用于分子性质分类.
- 为了减轻药物开发应用中的过度自信错误预测.
- 在一个有证据的深度学习框架内使用规范化流程引入一种新的方法.
主要方法:
- 在深度学习分类模型中,将标准Softmax函数替换为规范化流程.
- 采用了以证据为基础的深度学习方法,灵感来自后方网络.
- 评估了对合成数据集,ADMET预测和基于联体的虚拟查的战略.
主要成果:
- 与传统的软max模型相比,拟议的策略显著减少了过度自信的错误预测.
- 提高模型可靠性,识别销售外的样本.
- 在各种分子性质预测任务中表现出更好的性能.
结论:
- 有证据的深度学习与规范化流提供了一个强大的解决方案,用于不确定性估计在分子性质预测.
- 开发的框架有效地解决了药物发现当前深度学习模型的关键局限性.
- 这种方法为更安全,更有效的药物开发提供了有价值的工具.
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