Fan Zhehuan, Yu Jie, Zhang Xiang, Chen Yijie, Sun Shihui, Zhang Yuanyuan, Chen Mingan, Xiao Fu, Wu Wenyong, Li Xutong, Zheng Mingyue, Luo Xiaomin, Wang Dingyan
Drug 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.
University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China.
Patterns (N Y). 2024 May 8;5(6):100991. doi: 10.1016/j.patter.2024.100991. eCollection 2024 Jun 14.
Deep-learning-based classification models are increasingly used for predicting molecular properties in drug development. However, traditional classification models using the Softmax function often give overconfident mispredictions for out-of-distribution samples, highlighting a critical lack of accurate uncertainty estimation. Such limitations can result in substantial costs and should be avoided during drug development. Inspired by advances in evidential deep learning and Posterior Network, we replaced the Softmax function with a normalizing flow to enhance the uncertainty estimation ability of the model in molecular property classification. The proposed strategy was evaluated across diverse scenarios, including simulated experiments based on a synthetic dataset, ADMET predictions, and ligand-based virtual screening. The results demonstrate that compared with the vanilla model, the proposed strategy effectively alleviates the problem of giving overconfident but incorrect predictions. Our findings support the promising application of evidential deep learning in drug development and offer a valuable framework for further research.
基于深度学习的分类模型在药物研发中越来越多地用于预测分子性质。然而，使用Softmax函数的传统分类模型对于分布外样本往往会给出过度自信的错误预测，这凸显了准确不确定性估计的严重不足。这种局限性可能导致巨大成本，在药物研发过程中应予以避免。受证据深度学习和后验网络进展的启发，我们用归一化流取代了Softmax函数，以增强模型在分子性质分类中的不确定性估计能力。所提出的策略在各种场景中进行了评估，包括基于合成数据集的模拟实验、ADMET预测和基于配体的虚拟筛选。结果表明，与普通模型相比，所提出的策略有效地缓解了给出过度自信但错误预测的问题。我们的研究结果支持了证据深度学习在药物研发中的应用前景，并为进一步研究提供了有价值的框架。
