Triaging unpromising lead molecules early in the drug discovery process is essential for accelerating its pace while avoiding the costs of unwarranted biological and clinical testing. Accordingly, medicinal chemists have been trying for decades to develop metrics—ranging from heuristic measures to machine-learning models—that could rapidly distinguish potential drugs from small molecules that lack drug-like features. However, none of these metrics has gained universal acceptance and the very idea of ‘drug-likeness’ has recently been put into question. Here, we evaluate drug-likeness using different sets of descriptors and different state-of-the-art classifiers, reaching an out-of-sample accuracy of 87–88%. Remarkably, because these individual classifiers yield different Bayesian error distributions, their combination and selection of minimal-variance predictions can increase the accuracy of distinguishing drug-like from non-drug-like molecules to 93%. Because total variance is comparable with its aleatoric contribution reflecting irreducible error inherent to the dataset (as opposed to the epistemic contribution due to the model itself), this level of accuracy is probably the upper limit achievable with the currently known collection of drugs.

在药物开发过程的早期对没有前途的先导分子进行分类对于加快其步伐，同时避免不必要的生物学和临床测试费用至关重要。因此，药物化学家数十年来一直在尝试开发度量标准-从启发式度量到机器学习模型-可以快速将潜在药物与缺乏类似药物特征的小分子区分开。但是，这些指标均未获得普遍认可，“药物相似性”这一概念最近也受到质疑。在这里，我们使用不同的描述符集和不同的分类器评估药物相似性，样本外准确性达到87-88％。值得注意的是，由于这些单独的分类器会产生不同的贝叶斯误差分布，它们的结合以及对最小方差预测的选择可以将区分药物类分子与非药物类分子的准确性提高到93％。因为总方差与其反映出数据集固有的不可减少的误差的无意贡献（与由于模型本身引起的认知贡献相反）相当，所以这种准确性水平可能是当前已知药物集合可达到的上限。