ArticleFrontiers in bioinformatics2023
The promises of large language models for protein design and modeling.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Computational understanding of non-coding RNA pairwise interactions.Frontiers in artificial intelligence · 2026Article
- MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.Bioinformatics advances · 2026Article
- Protein language model-generated enzyme sequences exhibit high stability in molecular dynamics simulations.Frontiers in artificial intelligence · 2026Article
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
- Focused learning by antibody language models using preferential masking of non-templated regions.Patterns (New York, N.Y.) · 2025Article
- Large Language Models in Genomics-A Perspective on Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Emerging technologies transforming the future of global biosecurity.Frontiers in digital health · 2025Review
- Fine-tuning of conditional Transformers improvesComputational and structural biotechnology journal · 2025Article
- Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and machine learning.Computational and structural biotechnology journal · 2024Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- Learning Tools Using ChatGPT in the Biochemistry Class: Creating Notes and Performance on Exams.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular BiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.