Large language models for biomolecular analysis: From methods to applications

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Large language models for biomolecular analysis: From methods to applications

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Large language models for biomolecular analysis: From methods to applications

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

TrAC - Trends in Analytical Chemistry

ISSN

0165-9936

e-ISSN

—

Svazek periodika

171

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85182873304