The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac285.
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.
我们构建了一个蛋白质-蛋白质相互作用（PPI）靶向药物相似性数据集，并提出了一个深度分子生成框架，从种子化合物的特征中生成新的药物相似性分子。该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，并为 PPI 抑制剂的从头分子设计开发深度分子生成模型。首次将针对 PPI 的化合物的定量估计指标应用于从头设计 PPI 靶向化合物的分子生成模型的评估。我们的结果估计生成的分子具有更好的 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个最先进的分子生成模型相当的性能。通过化学空间分析表明，生成的分子与 iPPI-DB 抑制剂具有相似的化学空间。探索了基于肽特征的 PPI 抑制剂设计和基于配体的 PPI 抑制剂设计。最后，我们建议该框架将是 PPI 靶向治疗药物从头设计的重要一步。
