Large language models for biomolecular analysis: From methods to applications

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AIK9QJBT6" target="_blank" >RIV/00216208:11320/25:IK9QJBT6 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182873304&doi=10.1016%2fj.trac.2024.117540&partnerID=40&md5=ec9dfa4658b99fa5730c7dbcdf5d3ce8" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182873304&doi=10.1016%2fj.trac.2024.117540&partnerID=40&md5=ec9dfa4658b99fa5730c7dbcdf5d3ce8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.trac.2024.117540" target="_blank" >10.1016/j.trac.2024.117540</a>

Result language
angličtina
Original language name
Large language models for biomolecular analysis: From methods to applications
Original language description
Large language models (LLMs) are proving to be very useful in many fields, especially chemistry and biology, because of their amazing capabilities. Biomolecular data is often represented sequentially, much like textual data used to train LLMs. However, developing LLMs from scratch requires a substantial amount of data and computational resources, which may not be feasible for most researchers. A more workable solution to this problem is to change the inputs or parameters so that the previously trained general LLMs can pick up the specific knowledge needed for biomolecular analysis. These adaption strategies lower the amount of data and hardware needed, providing a more affordable option. This review provides the introduction of two popular LLM adaptation techniques: fine-tuning and prompt engineering, along with their uses in the analysis of molecules, proteins, and genes. A thorough overview of current common datasets and pre-trained models is also provided. This review outlines the possible advantages and difficulties of LLMs for biomolecular analysis, opening the door for chemists and biologists to effectively utilize LLMs in their future studies. © 2024 Elsevier B.V.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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