Multi-task Neural Networks for QSAR Predictions
George E. Dahl Navdeep Jaitly Ruslan Salakhutdinov Department of Computer Science, University of Toronto, 6 King’s College Rd, Toronto, Ontario M5S 3G4, Canada
Abstract
Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR)studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests. However, a variety of new neural net techniques along with successful applications in other domains have renewed interest in network approaches. In this work, inspired by the winning team’s use of neural networks in a recent QSAR competition, we used an artificial neural network to learn a function that predicts activities of compounds for multiple assays at the same time. We conducted experiments leveraging recent methods for dealing with overfitting in neural networks as well as other tricks from the neural networks literature. We compared our methods to alternative methods reported to perform well on these tasks and found that our neural net methods provided superior performance.
中文速览
药物研发中，研究人员需要通过定量构效关系（QSAR/QSPR）模型，在不做真实实验的情况下预测化合物的生物活性，而现有方法（如随机森林）在数据量有限时性能提升空间受限。本文受Merck QSAR竞赛冠军方案启发，将深度神经网络（deep neural network, DNN）引入QSAR建模，并创新性地采用多任务学习（multi-task learning）框架，让一个模型同时预测多个实验检测任务的活性，从而让不同任务共享特征提取层、互相补充数据。研究还系统引入了dropout、早停、权重惩罚等现代正则化技巧来对抗过拟合，在多组真实药物筛选数据集上与随机森林等主流方法进行了对比实验，结果显示多任务深度神经网络在预测精度上显著优于这些基线方法。这项工作表明，深度学习在小样本、多任务的药物活性预测场景中具有切实价值，为后续将深度学习大规模应用于药物发现提供了早期实证依据。
原文 arXiv:1406.1231;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1406.1231v1