Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries.
Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries.
复制标题
以脚手架和大麻素CB2靶标特异性小分子subliraries的方式进行人工智能深度学习分子生成模型。
作者：
Bian Y;Xie XQ
设计和产生高质量的靶向和支架特异性小分子是发现独特和有效的生物活性药物分子的重要策略。为了实现这一目标，作者开发了深度学习分子生成模型（DeepMGM），并将其应用于支架聚焦小分子库的从头分子生成。在这项研究中，使用长短期记忆（LSTM）单元的递归神经网络（RNN）用药物样分子进行训练，以产生通用模型（g-DeepMGM）。吲哚和嘌呤支架上的取样实践说明了基于机器智能创建以支架为中心的化学库的可行性。随后，在已知CB 2配体的迁移学习过程之后，构建了大麻素受体2（CB 2）的靶特异性模型（t-DeepMGM）。采样结果可以呈现与所报道的活性分子相似的性质。最后，训练一个机器人并将其连接到DeepMGM上，以形成计算机分子设计测试循环。进行药物化学合成和生物学验证以进一步研究生成结果，表明XIE 9137被鉴定为CB 2的潜在变构调节剂。这项研究展示了深度学习智能的最新进展如何有利于药物发现，特别是在从头分子设计和化学库生成方面。
Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule generation model (DeepMGM) and applied it for the de novo molecular generation of scaffold-focused small-molecule libraries. In this study, a recurrent neural network (RNN) using long short-term memory (LSTM) units was trained with drug-like molecules to result in a general model (g-DeepMGM). Sampling practices on indole and purine scaffolds illustrate the feasibility of creating scaffold-focused chemical libraries based on machine intelligence. Subsequently, a target-specific model (t-DeepMGM) for cannabinoid receptor 2 (CB2) was constructed following the transfer learning process of known CB2 ligands. Sampling outcomes can present similar properties to the reported active molecules. Finally, a discriminator was trained and attached to the DeepMGM to result in an in silico molecular design-test circle. Medicinal chemistry synthesis and biological validation was performed to further investigate the generation outcome, showing that XIE9137 was identified as a potential allosteric modulator of CB2. This study demonstrates how recent progress in deep learning intelligence can benefit drug discovery, especially in de novo molecular design and chemical library generation.
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DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
DOI：
10.1038/nrd2760
发表时间：
2009-01
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
通讯作者：
--
影响因子：
21.8
作者：
通讯作者：
--
影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
56.9
作者：
Coley, Connor W.;Thomas, Dale A., III;Jensen, Klavs F.
通讯作者：
Jensen, Klavs F.