深度学习用于虚拟筛选：使用ROC成本函数的五个理由
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions
摘要：
计算机辅助药物发现是现代药物开发的重要组成部分。其中，深度学习已经成为一种重要的工具，用于快速筛选数十亿个含有所需化学特征的分子。尽管它很重要，但在训练这些模型时仍然存在巨大的挑战，例如严重的类别不平衡，高决策阈值以及某些数据集中缺乏地面真值标签。在这项工作中，我们主张在这种情况下直接优化受试者操作特征（ROC），因为它对类不平衡的鲁棒性，它在不同决策阈值上妥协的能力，在这种妥协中影响相对权重的一定自由度，对典型基准测量的保真度，以及与积极/未标记学习的等效性。我们还为基于ROC的成本函数提出了新的训练方案（连贯的小批量安排和批外样本的使用），以及基于logAUC度量的成本函数，以促进早期富集（即在高决策阈值下提高性能，这是合成预测命中化合物时经常需要的）。我们证明，这些方法在一系列PubChem高通量筛选数据集上的表现优于标准的深度学习方法，这些数据集代表了主要药物靶向家族的现实和多样化的药物发现活动。
英文摘要：
Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. In this work we argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance, its ability to compromise over different decision thresholds, certain freedom to influence the relative weights in this compromise, fidelity to typical benchmarking measures, and equivalence to positive/unlabeled learning. We also propose new training schemes (coherent mini-batch arrangement, and usage of out-of-batch samples) for cost functions based on the ROC, as well as a cost function based on the logAUC metric that facilitates early enrichment (i.e. improves performance at high decision thresholds, as often desired when synthesizing predicted hit compounds). We demonstrate that these approaches outperform standard deep learning approaches on a series of PubChem high-throughput screening datasets that represent realistic and diverse drug discovery campaigns on major drug target families.
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