Novartis Institutes for BioMedical Research, Novartis Pharma AG, Postfach, 4002 Basel, Switzerland.
J Med Chem. 2023 Oct 26;66(20):14047-14060. doi: 10.1021/acs.jmedchem.3c01083. Epub 2023 Oct 10.
Early assessment of the potential of a series of compounds to deliver a drug is one of the major challenges in computer-assisted drug design. The goal is to identify the right chemical series of compounds out of a large chemical space to then subsequently prioritize the molecules with the highest potential to become a drug. Although multiple approaches to assess compounds have been developed over decades, the quality of these predictors is often not good enough and compounds that agree with the respective estimates are not necessarily druglike. Here, we report a novel deep learning approach that leverages large-scale predictions of ∼100 ADMET assays to assess the potential of a compound to become a relevant drug candidate. The resulting score, which we termed bPK score, substantially outperforms previous approaches and showed strong discriminative performance on data sets where previous approaches did not.
早期评估一系列化合物提供药物的潜力是计算机辅助药物设计中的主要挑战之一。目标是从大量化学空间中识别出正确的化合物系列，然后对具有最大成为药物潜力的分子进行优先级排序。尽管几十年来已经开发出了多种评估化合物的方法，但这些预测器的质量往往不够好，并且与各自估计值相符的化合物不一定具有药物特性。在这里，我们报告了一种新的深度学习方法，该方法利用了对约 100 项 ADMET 测定的大规模预测来评估化合物成为相关药物候选物的潜力。所得分数，我们称之为 bPK 分数，大大优于以前的方法，并且在以前的方法表现不佳的数据集上表现出了很强的区分性能。
