Abstract：
We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.
摘要：
我们构建了一个蛋白质-蛋白质相互作用（PPI）靶向的药物相似度数据集，并提出了一个深层的分子生成框架，以从种子化合物的特征中生成新的药物相似度分子。这个框架从已发表的分子生成模型中获得灵感，使用与PPI抑制剂相关的关键特征作为输入，并开发用于PPI抑制剂从头分子设计的深层分子生成模型。第一次,以PPI为目标的化合物的定量估计指数被应用于PPI靶向化合物从头设计的分子生成模型的评估。我们的结果估计产生的分子具有更好的PPI靶向药物相似性和药物相似性。此外,我们的模型还表现出与其他几种最先进的分子生成模型相当的性能。如化学空间分析所证明的,所产生的分子与iPPI-DB抑制剂共享化学空间。探索了PPI抑制剂的肽表征设计和基于配体的PPI抑制剂设计。最后,我们建议,该框架将是PPI靶向治疗的从头设计的重要一步.
