دورية أكاديمية

The promises of large language models for protein design and modeling

التفاصيل البيبلوغرافية
العنوان: The promises of large language models for protein design and modeling
المؤلفون: Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, Peter N. Robinson
المصدر: Frontiers in Bioinformatics, Vol 3 (2023)
بيانات النشر: Frontiers Media S.A., 2023.
سنة النشر: 2023
المجموعة: LCC:Computer applications to medicine. Medical informatics
مصطلحات موضوعية: large language models, protein modeling, protein design, protein engineering, transformers, deep learning, Computer applications to medicine. Medical informatics, R858-859.7
الوصف: The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins” invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2673-7647
Relation: https://www.frontiersin.org/articles/10.3389/fbinf.2023.1304099/full; https://doaj.org/toc/2673-7647
DOI: 10.3389/fbinf.2023.1304099
URL الوصول: https://doaj.org/article/ed73130681584108962657b68230b70f
رقم الأكسشن: edsdoj.73130681584108962657b68230b70f
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:26737647
DOI:10.3389/fbinf.2023.1304099