The clinical efficacy and safety of a drug is determined by its molecular properties and targets in humans. However, proteome-wide evaluation of all compounds in humans, or even animal models, is challenging. In this study, we present an unsupervised pretraining deep learning framework, named ImageMol, pretrained on 10 million unlabelled drug-like, bioactive molecules, to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabelled molecular images on the basis of local and global structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (that is, the drug’s metabolism, brain penetration and toxicity) and molecular target profiles (that is, beta-secretase enzyme and kinases) across 51 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences. Via ImageMol, we identified candidate clinical 3C-like protease inhibitors for potential treatment of COVID-19.

药物的临床疗效和安全性取决于其在人体中的分子特性和靶点。然而，对人类甚至动物模型中的所有化合物进行蛋白质组范围的评估具有挑战性。在这项研究中，我们提出了一个名为 ImageMol 的无监督预训练深度学习框架，对 1000 万个未标记的类药物生物活性分子进行了预训练，以预测候选化合物的分子靶点。ImageMol 框架旨在根据来自像素的分子的局部和全局结构特征，从未标记的分子图像中预训练化学表征。我们展示了 ImageMol 在评估分子特性（即药物的代谢、脑渗透和毒性）和分子靶标概况（即，β-分泌酶和激酶）跨越 51 个基准数据集。ImageMol 在国家转化科学推进中心的 13 个高通量实验数据集中显示了识别抗 SARS-CoV-2 分子的高精度。通过 ImageMol，我们确定了用于潜在治疗 COVID-19 的候选临床 3C 样蛋白酶抑制剂。