A review of transformer models in drug discovery and beyond.
10.1016/j.jpha.2024.101081
- Author:
Jian JIANG
1
;
Long CHEN
1
;
Lu KE
1
;
Bozheng DOU
1
;
Chunhuan ZHANG
1
;
Hongsong FENG
2
;
Yueying ZHU
1
;
Huahai QIU
1
;
Bengong ZHANG
1
;
Guo-Wei WEI
2
Author Information
1. Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, China.
2. Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
- Publication Type:Review
- Keywords:
Chemical language understanding;
Drug discovery;
Molecular dynamics;
Protein design;
Transformer
- From:
Journal of Pharmaceutical Analysis
2025;15(6):101081-101081
- CountryChina
- Language:English
-
Abstract:
Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
