Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors | |
Yang, Lijuan1,2,3,4; Yang, Guanghui1,4; Bing, Zhitong1,4; Tian, Yuan1,5; Niu, Yuzhen6; Huang, Liang2; Yang, Lei1,4 | |
刊名 | ACS OMEGA |
2021-12-14 | |
卷号 | 6期号:49页码:33864-33873 |
ISSN号 | 2470-1343 |
DOI | 10.1021/acsomega.1c05145 |
通讯作者 | Niu, Yuzhen(niuyzh12@lzu.edu.cn) ; Yang, Lei(lyang_imp@outlook.com) |
英文摘要 | The de novo drug design based on SMILES format is a typical sequence-processing problem. Previous methods based on recurrent neural network (RNN) exhibit limitation in capturing long-range dependency, resulting in a high invalid percentage in generated molecules. Recent studies have shown the potential of Transformer architecture to increase the capacity of handling sequence data. In this work, the encoder module in the Transformer is used to build a generative model. First, we train a Transformer-encoder-based generative model to learn the grammatical rules of known drug molecules and a predictive model to predict the activity of the molecules. Subsequently, transfer learning and reinforcement learning were used to fine-tune and optimize the generative model, respectively, to design new molecules with desirable activity. Compared with previous RNN-based methods, our method has improved the percentage of generating chemically valid molecules (from 95.6 to 98.2%), the structural diversity of the generated molecules, and the feasibility of molecular synthesis. The pipeline is validated by designing inhibitors against the human BRAF protein. Molecular docking and binding mode analysis showed that our method can generate small molecules with higher activity than those carrying ligands in the crystal structure and have similar interaction sites with these ligands, which can provide new ideas and suggestions for pharmaceutical chemists. |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA21010202] |
WOS关键词 | PROTEINS ; DESIGN |
WOS研究方向 | Chemistry |
语种 | 英语 |
出版者 | AMER CHEMICAL SOC |
WOS记录号 | WOS:000757388000057 |
资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.186/handle/113462/142062] |
专题 | 中国科学院近代物理研究所 |
通讯作者 | Niu, Yuzhen; Yang, Lei |
作者单位 | 1.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China 2.Lanzhou Univ, Sch Phys & Technol, Lanzhou 730000, Peoples R China 3.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China 4.Guangdong Lab, Adv Energy Sci & Technol, Huizhou 516000, Peoples R China 5.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 6.Shandong Univ Technol, Shandong Prov Res Ctr Bioinformat Engn & Tech, Sch Life Sci, Zibo 255000, Peoples R China |
推荐引用方式 GB/T 7714 | Yang, Lijuan,Yang, Guanghui,Bing, Zhitong,et al. Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors[J]. ACS OMEGA,2021,6(49):33864-33873. |
APA | Yang, Lijuan.,Yang, Guanghui.,Bing, Zhitong.,Tian, Yuan.,Niu, Yuzhen.,...&Yang, Lei.(2021).Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors.ACS OMEGA,6(49),33864-33873. |
MLA | Yang, Lijuan,et al."Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors".ACS OMEGA 6.49(2021):33864-33873. |
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