MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules
文献类型：期刊论文
| 作者 | Liu, Xiaohong4,5,6,7; Zhang, Wei5,6; Tong, Xiaochu5,6; Zhong, Feisheng5,6; Li, Zhaojun4; Xiong, Zhaoping5,6,7; Xiong, Jiacheng5,6; Wu, Xiaolong2,6; Fu, Zunyun6; Tan, Xiaoqin3,5,6 |
| 刊名 | JOURNAL OF CHEMINFORMATICS
 |
| 出版日期 | 2023-04-08 |
| 卷号 | 15期号:1页码:42 |
| 关键词 | De novo molecule design Generative models Deep learning Virtual screening Compound quality control |
| DOI | 10.1186/s13321-023-00711-1 |
| 文献子类 | Article |
| 英文摘要 | Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models. |
| WOS关键词 | DISCOVERY |
| WOS研究方向 | Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000966066000001 |
| 出版者 | BMC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/309651]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Li, Xutong; Zheng, Mingyue |
| 作者单位 | 1.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 310024, Peoples R China 2.East China Univ Sci & Technol, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China; 3.ByteDance AI Lab, 1999 Yishan Rd, Shanghai 201103, Peoples R China; 4.AlphaMa Inc, 108, Yuxin Rd,Suzhou Ind Pk, Suzhou 215128, Peoples R China; 5.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China; 6.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China; 7.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Xiaohong,Zhang, Wei,Tong, Xiaochu,et al. MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules[J]. JOURNAL OF CHEMINFORMATICS,2023,15(1):42. |
| APA | Liu, Xiaohong.,Zhang, Wei.,Tong, Xiaochu.,Zhong, Feisheng.,Li, Zhaojun.,...&Zheng, Mingyue.(2023).MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.JOURNAL OF CHEMINFORMATICS,15(1),42. |
| MLA | Liu, Xiaohong,et al."MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules".JOURNAL OF CHEMINFORMATICS 15.1(2023):42. |
入库方式： OAI收割
来源：上海药物研究所
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