Journal article
Large language models reshaping molecular biology and drug development
Chemical biology & drug design, v 103(6), e14568
Jun 2024
Abstract
Abstract The utilization of large language models (LLMs) has become a significant advancement in the domains of medicine and clinical informatics, providing a revolutionary potential for scientific breakthroughs and customized therapies. LLM models are trained on large datasets and exhibit the capacity to comprehend and analyze intricate biological data, encompassing genomic sequences, protein structures, and clinical health records. With the utilization of their comprehension of the language of biology, they possess the ability to reveal concealed patterns and insights that may evade human researchers. LLMs have been shown to positively impact various aspects of molecular biology, including the following: genomic analysis, drug development, precision medicine, biomarker development, experimental design, collaborative research, and accessibility to specialized expertise. However, it is imperative to acknowledge and tackle the obstacles and ethical implications involved. The careful consideration of data bias and generalization, data privacy and security, explainability and interpretability, and ethical concerns around responsible application is vital. The successful resolution of these obstacles will enable us to fully utilize the capabilities of LLMs, leading to substantial progress in the fields of molecular biology and pharmaceutical research. This progression also has the ability to bolster influential impacts for both the individual and the broader community.
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Details
- Title
- Large language models reshaping molecular biology and drug development
- Creators
- Satvik Tripathi - Drexel UniversityKyla Gabriel - Harvard Medical SchoolPushpendra Kumar Tripathi - University of LucknowEdward Kim - Drexel University
- Publication Details
- Chemical biology & drug design, v 103(6), e14568
- Publisher
- Wiley
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001251167800001
- Scopus ID
- 2-s2.0-85196318707
- Other Identifier
- 991021888915704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemistry & Molecular Biology
- Chemistry, Medicinal