Insilico Medicine , Rockville , Maryland 20850 , United States.
National Research University Higher School of Economics , Moscow 101000 , Russia.
Mol Pharm. 2018 Oct 1;15(10):4398-4405. doi: 10.1021/acs.molpharmaceut.8b00839. Epub 2018 Sep 19.
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis. We apply the proposed model to generate a novel inhibitor of Janus kinase 3, implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and showed good activity and selectivity.
现代计算方法和机器学习技术加速了新药的发明。生成模型可以在数小时内发现新颖的分子结构，而传统的药物发现管道则需要数月的工作。在本文中，我们提出了一种新的生成架构，即纠缠条件对抗自动编码器，它可以根据各种特性（如对特定蛋白质的活性、溶解度或合成难易度）生成分子结构。我们应用所提出的模型生成了一种新型 Janus 激酶 3 抑制剂，该抑制剂与类风湿性关节炎、银屑病和白癜风有关。所发现的分子在体外进行了测试，表现出良好的活性和选择性。
