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Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2020-06-08 , DOI: 10.1186/s13321-020-00446-3
Xuanyi Li , Yinqiu Xu , Hequan Yao , Kejiang Lin

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

中文翻译:

基于递归神经网络的化学空间探索:在发现激酶抑制剂中的应用

随着药物发现中人工智能(AI)的兴起,从头分子生成提供了探索化学空间的新方法。但是,由于从头分子产生方法依赖大量已知分子,因此产生的分子可能存在新颖性问题。在药物化学等竞争激烈的领域,例如激酶抑制剂的发现,新颖性很重要。在这项研究中,基于递归神经网络的从头分子生成被用于发现激酶抑制剂的新化学空间。在申请过程中,评估了实用性,并找到了新的灵感。随着成功发现一种有效的Pim1抑制剂和两种抑制CDK4的先导化合物,基于AI的分子生成显示了药物发现和开发的潜力。
更新日期:2020-06-08
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