Multimodal pre-training models of molecular representation for drug discovery
文献类型：期刊论文
| 作者 | Wang, Xiaoqi2,3; Wang, Chuanshi2,3; Ji, Boya1; Wang, Junwen5; Zheng, Mingyue4; Song, Lingyun2,3; Peng, Shaoliang1; Shang, Xuequn2,3 |
| 刊名 | NATIONAL SCIENCE REVIEW
 |
| 出版日期 | 2026 |
| 卷号 | 13期号:1页码:18 |
| 关键词 | self-supervised learning multimodal pre-training model Transformer molecular representation drug discovery |
| ISSN号 | 2095-5138 |
| DOI | 10.1093/nsr/nwaf495 |
| 英文摘要 | With the great success of large language models in natural language processing, self-supervised pre-training models have emerged as an important technique in drug discovery. In particular, multimodal pre-training models have opened a new avenue for drug discovery. The experience and ideas from previous works can provide important reference points for further research in drug discovery. Therefore, this review summarizes the foundation of multimodal pre-training models and their progress in the field of drug discovery. We emphasize the adaptability between various modalities and network frameworks or pre-training tasks. At the same time, we summarize the difference and relevance between various modalities or pre-training models. Importantly, we identify two increasing trends that may serve as reference points for future research. Specifically, Transformers and graph neural networks are often integrated as encoders and then combined with multiple pre-training tasks to learn cross-scale molecular representation, thereby promoting the accuracy of drug discovery. In addition, molecular captions as brief biomedical text provide a bridge for collaboration between drug discovery and large language models. Finally, we discuss the challenges of multimodal pre-training models in drug discovery, and explore future opportunities. A systematic review summarizing the foundation of multimodal pre-training models and their progress in molecular representation of drug discovery. |
| WOS关键词 | NETWORKS |
| 资助项目 | Fundamental Research Funds for the Central Universities[D5000240148] ; Key Technologies Research and Development Program[2023B1111030004] ; Hunan Science and Technology Innovation Plan[2025ZYJ003] ; National Natural Science Foundation of China[62402390] ; National Natural Science Foundation of China[62433016] ; Innovative Research Group Project[2024JJ1002] ; Key Research and Development Program of Hunan Province of China[2023GK2004] ; Key Research and Development Program of Hunan Province of China[2023SK2059] ; Key Research and Development Program of Hunan Province of China[2023SK2060] ; National Key Research and Development Program of China[2023YFC3503400] ; National Key Research and Development Program of China[2022YFC3400400] ; FDCT[62361166662] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001660343500001 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/322584]  |
| 专题 | 国家级研究中心_原创新药研究全国重点实验室 |
| 通讯作者 | Peng, Shaoliang; Shang, Xuequn |
| 作者单位 | 1.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China 2.Northwestern Polytech Univ, Key Lab Big Data Storage & Management, Minist Ind & Informat Technol, Xian 710129, Peoples R China 3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Peoples R China 4.Chinese Acad Sci, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China 5.Univ Hong Kong, Fac Dent, Div Appl Oral Sci & Community Dent Care, Hong Kong 999077, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xiaoqi,Wang, Chuanshi,Ji, Boya,et al. Multimodal pre-training models of molecular representation for drug discovery[J]. NATIONAL SCIENCE REVIEW,2026,13(1):18. |
| APA | Wang, Xiaoqi.,Wang, Chuanshi.,Ji, Boya.,Wang, Junwen.,Zheng, Mingyue.,...&Shang, Xuequn.(2026).Multimodal pre-training models of molecular representation for drug discovery.NATIONAL SCIENCE REVIEW,13(1),18. |
| MLA | Wang, Xiaoqi,et al."Multimodal pre-training models of molecular representation for drug discovery".NATIONAL SCIENCE REVIEW 13.1(2026):18. |
入库方式： OAI收割
来源：上海药物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。