ReviewBriefings in bioinformatics2023
Generative models for protein sequence modeling: recent advances and future directions.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
- Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Extraction of the polysorbate 20 and 80 fingerprint via generative modeling.International journal of pharmaceutics: X · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Controllable Generation of Pathogen-Specific Antimicrobial Peptides Through Knowledge-Aware Prompt Diffusion Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Design of overlapping genes using deep generative models of protein sequences.bioRxiv : the preprint server for biology · 2025Article
- CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Review
- Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.Journal of chemical information and modeling · 2025Article
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.
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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.