The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery.

在早期药物开发过程中，使用新型支架检索命中/先导化合物是一项重要但具有挑战性的任务。已经提出了各种生成模型来创建类药物分子。然而，这些生成模型使用新型支架设计湿实验室验证和目标特异性分子的能力几乎没有得到验证。我们在此提出了一种生成式深度学习 (GDL) 模型，一种分布学习条件递归神经网络 (cRNN)，用于为给定的生物靶标生成量身定制的虚拟化合物库。然后将 GDL 模型应用于 RIPK1。针对生成的定制化合物库进行虚拟筛选和随后的生物活性评估导致发现了一种具有先前未报道的支架 RI-962 的强效和选择性 RIPK1 抑制剂。该化合物在保护细胞免于坏死性凋亡方面显示出有效的体外活性，并且在两种炎症模型中具有良好的体内功效。总的来说，这些发现证明了我们的 GDL 模型在使用未报告的支架生成命中/先导化合物方面的能力，突出了深度学习在药物发现中的巨大潜力。