Large language models facilitating modern molecular biology and novel drug development
The latest breakthroughs in information technology and biotechnology have catalyzed a revolutionary shift within the modern healthcare landscape, with notable impacts from artificial intelligence (AI) and deep learning (DL). Particularly noteworthy is the adept application of large language models (...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1458739/full |
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| _version_ | 1846110498506932224 |
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| author | Xiao-huan Liu Zhen-hua Lu Tao Wang Fei Liu |
| author_facet | Xiao-huan Liu Zhen-hua Lu Tao Wang Fei Liu |
| author_sort | Xiao-huan Liu |
| collection | DOAJ |
| description | The latest breakthroughs in information technology and biotechnology have catalyzed a revolutionary shift within the modern healthcare landscape, with notable impacts from artificial intelligence (AI) and deep learning (DL). Particularly noteworthy is the adept application of large language models (LLMs), which enable seamless and efficient communication between scientific researchers and AI systems. These models capitalize on neural network (NN) architectures that demonstrate proficiency in natural language processing, thereby enhancing interactions. This comprehensive review outlines the cutting-edge advancements in the application of LLMs within the pharmaceutical industry, particularly in drug development. It offers a detailed exploration of the core mechanisms that drive these models and zeroes in on the practical applications of several models that show great promise in this domain. Additionally, this review delves into the pivotal technical and ethical challenges that arise with the practical implementation of LLMs. There is an expectation that LLMs will assume a more pivotal role in the development of innovative drugs and will ultimately contribute to the accelerated development of revolutionary pharmaceuticals. |
| format | Article |
| id | doaj-art-b56666f265da495c82abc6c24e0a414c |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-b56666f265da495c82abc6c24e0a414c2024-12-24T06:36:28ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-12-011510.3389/fphar.2024.14587391458739Large language models facilitating modern molecular biology and novel drug developmentXiao-huan Liu0Zhen-hua Lu1Tao Wang2Fei Liu3School of Biological Science, Jining Medical University, Jining, ChinaCollege of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaSchool of Biological Science, Jining Medical University, Jining, ChinaSchool of Biological Science, Jining Medical University, Jining, ChinaThe latest breakthroughs in information technology and biotechnology have catalyzed a revolutionary shift within the modern healthcare landscape, with notable impacts from artificial intelligence (AI) and deep learning (DL). Particularly noteworthy is the adept application of large language models (LLMs), which enable seamless and efficient communication between scientific researchers and AI systems. These models capitalize on neural network (NN) architectures that demonstrate proficiency in natural language processing, thereby enhancing interactions. This comprehensive review outlines the cutting-edge advancements in the application of LLMs within the pharmaceutical industry, particularly in drug development. It offers a detailed exploration of the core mechanisms that drive these models and zeroes in on the practical applications of several models that show great promise in this domain. Additionally, this review delves into the pivotal technical and ethical challenges that arise with the practical implementation of LLMs. There is an expectation that LLMs will assume a more pivotal role in the development of innovative drugs and will ultimately contribute to the accelerated development of revolutionary pharmaceuticals.https://www.frontiersin.org/articles/10.3389/fphar.2024.1458739/fullartificial intelligencelarge language modelsdrug developmentChatGPTprotein structure prediction |
| spellingShingle | Xiao-huan Liu Zhen-hua Lu Tao Wang Fei Liu Large language models facilitating modern molecular biology and novel drug development Frontiers in Pharmacology artificial intelligence large language models drug development ChatGPT protein structure prediction |
| title | Large language models facilitating modern molecular biology and novel drug development |
| title_full | Large language models facilitating modern molecular biology and novel drug development |
| title_fullStr | Large language models facilitating modern molecular biology and novel drug development |
| title_full_unstemmed | Large language models facilitating modern molecular biology and novel drug development |
| title_short | Large language models facilitating modern molecular biology and novel drug development |
| title_sort | large language models facilitating modern molecular biology and novel drug development |
| topic | artificial intelligence large language models drug development ChatGPT protein structure prediction |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1458739/full |
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