论文标题
基于多视图深度学习的分子设计和结构优化加速了SARS-COV-2抑制剂发现
Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery
论文作者
论文摘要
在这项工作中，我们提出了Medico，这是一种用于分子生成，结构优化和SARS-COV-2抑制剂发现的多视图深生成模型。据我们所知，Medico是第一类图形生成模型，它可以生成类似于目标分子结构的分子图，具有多视图表示学习框架，可以从目标分子拓扑和几何学中充分和适应地学习全面的结构词。我们表明，在基准测试比较下生成有效，独特和新颖的分子方面，我们的Medico明显优于最新方法。特别是，我们展示了多视图深度学习模型，使我们不仅能够生成与靶向分子结构相似的分子，而且可以生成具有所需化学特性的分子，也证明了模型在深入探索化学空间方面具有很强的能力。此外，案例研究结果是SARS-COV-2主要蛋白酶（MPRO）的靶向分子产生的结果表明，通过将分子对接整合到我们的模型中，我们成功地生成了具有MPRO的所需药物样性能的新的小分子，从而潜在地加速了Nove de Novo de Novo de Novo de Novo de de de de de de de de de de de de de de de de de de de de goVID 19 drugs。此外，我们将Medico应用于三种众所周知的MPRO抑制剂（N3、11A和GC376）的结构优化，并在其与MPRO的结合亲密关系方面提高了约88％，这证明了我们模型在SARS-COV-2感染的疗法中的应用价值。
In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. In particular, we showcase the multi-view deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties, demonstrating the strong capability of our model in exploring the chemical space deeply. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs. Further, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.
