Advanced graph and sequence neural networks for molecular property prediction and drug discovery
Advanced graph and sequence neural networks for molecular property prediction and drug discovery
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用于分子性质预测和药物发现的高级图和序列神经网络
DOI：
10.1093/bioinformatics/btac112
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发表时间：
2022-02-18
期刊：
影响因子：
5.8
通讯作者：
Ji, Shuiwang
中科院分区：
文献类型：
--
作者：
Wang, Zhengyang;Liu, Meng;Ji, Shuiwang
动机：分子的性质是其功能的指示，因此在许多应用中是有用的。随着深度学习方法的进步，预测分子性质的计算方法正在获得越来越大的动力。然而，目前缺乏定制的先进的方法和全面的工具来完成这一任务。结果：在这里，我们开发了一套全面的机器学习方法和工具，跨越不同的计算模型，分子表示和损失函数的分子性质预测和药物发现。具体来说，我们代表分子的图形和序列。基于这些表示，我们开发了新的深度模型，用于从分子图和序列中学习。为了有效地从高度不平衡的数据集中学习，我们开发了高级损失函数，优化了精确召回曲线（PRCs）和受试者操作特征（ROC）曲线下的区域。总而言之，我们的工作不仅是一个全面的工具，而且还有助于开发新的和先进的图形和序列学习方法。在线和离线抗生素发现和分子性质预测任务的结果表明，我们的方法实现了一致的改进，比以前的方法。特别是，我们的方法在与COVID-19相关的药物发现的AI Cures公开挑战中，在ROC-AUC（曲线下面积）和PRC-AUC方面均获得了第一名。
Motivation: Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep-learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently.Results: Here, we develop a suite of comprehensive machine-learning methods and tools spanning different computational models, molecular representations and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves (PRCs) and receiver operating characteristic (ROC) curves. Altogether, our work not only serves as a comprehensive tool, but also contributes toward developing novel and advanced graph and sequence-learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC (area under curve) and PRC-AUC on the AI Cures open challenge for drug discovery related to COVID-19.