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These vectors serve as input for machine learning models and facilitate the prediction and analysis of molecular attributes, functions, and reactions. The advent of foundation models has introduced both new opportunities and challenges to MRL. These models have improved generalizability and migration in scarce data. Through pretraining and fine-tuning, foundation models can be adapted to various domains. Their robust encoding and generative abilities also allow the transformation of molecular data into more expressive forms. This paper provides a detailed review of current mainstream molecular descriptors and datasets, focusing primarily on the representation of small molecules while excluding larger molecules such as proteins and peptides. It classifies foundation models into two primary categories based on the form of input: unimodal-based and multimodal-based models. For each category, representative models are identified and their advantages and disadvantages evaluated. Moreover, we systematically summarize four core pretraining strategies for MRL foundation models, analyzing their task designs, applicable scenarios, and impacts on downstream performance. In addition, the application of molecular representation foundation models in drug discovery and development is discussed, together with the current status of model interpretability. The paper concludes with insights into the future directions of MRL foundation models.<\/jats:p>","DOI":"10.1093\/bib\/bbaf703","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T13:01:56Z","timestamp":1766149316000},"source":"Crossref","is-referenced-by-count":2,"title":["A systematic review of molecular representation learning foundation models"],"prefix":"10.1093","volume":"27","author":[{"given":"Bosheng","family":"Song","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, 116 Lushan South Road, Yuelu District, 410086 Changsha,","place":["China"]}]},{"given":"Jiayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, 116 Lushan South Road, Yuelu District, 410086 Changsha,","place":["China"]}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, 116 Lushan South Road, Yuelu District, 410086 Changsha,","place":["China"]}]},{"given":"Yuansheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, 116 Lushan South Road, Yuelu District, 410086 Changsha,","place":["China"]}]},{"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, 116 Lushan South Road, Yuelu District, 410086 Changsha,","place":["China"]}]},{"given":"Sisi","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Chinese Medicine , Hong Kong Baptist University, 15 Baptist University Road, Kowloon Tong, Kowloon, Hong Kong SAR 999077,","place":["China"]}]},{"given":"Xia","family":"Zhen","sequence":"additional","affiliation":[{"name":"National Laboratory for Parallel and Distributed Processing , School of Computer, 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