The promises of large language models for protein design and modeling

N/ACitations
Citations of this article
39Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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.

Cite

CITATION STYLE

APA

Valentini, G., Malchiodi, D., Gliozzo, J., Mesiti, M., Soto-Gomez, M., Cabri, A., … Robinson, P. N. (2023). The promises of large language models for protein design and modeling. Frontiers in Bioinformatics, 3. https://doi.org/10.3389/fbinf.2023.1304099

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free