ReviewNature biotechnology2024
Machine learning for functional protein design.
Review in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 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
105 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.Synthetic and systems biotechnology · 2027Article
- Enzyme-mediated remodeling of the extracellular matrix and glycocalyx to enhance immunotherapy in solid tumors.Bioengineering & translational medicine · 2026Review
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- Recombinant Thermostable DNA Polymerases: Current Approaches to Production, Molecular Engineering, and Applications in Biotechnology and Diagnostics.International journal of molecular sciences · 2026Review
- A unified predictor of protein stability changes across all mutation typesChemical science · 2026Article
- Evolutionary profiles for protein fitness prediction.Bioinformatics (Oxford, England) · 2026Article
- Using enantioselective biosensors to evolve asymmetric biocatalysts.Nature chemical biology · 2026Article
- FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning.Briefings in bioinformatics · 2026Article
- Learning continuous activation fields from microscopic SEM images of lycoperdioid fungi via CNN-guided neural operator modeling.Scientific reports · 2026Article
- Three Decades of China's Bt Cotton: Achievements and Insights.Plant biotechnology journal · 2026Review
- Genuine Directed Evolution In Test Tube (GENie).bioRxiv : the preprint server for biology · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- Intrinsically disordered protein droplet-enhanced oligonucleotide assembly enables rapid oligonucleotide-to-protein expression.Nucleic acids research · 2026Article
- Recent Progress in Gain Materials for Microlasers and Modern Digital Approaches for Biophotonics: From Dyes to Semiconductors.Micromachines · 2026Review
- Direct evidence of acid-driven protein desolvation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Accelerated discovery of highly active enzyme nanohybrids with parallelized Bayesian optimization in hybrid space.Nature communications · 2026Article
- Peptide-functionalized nanoparticles for brain-targeted therapeutics.Drug delivery and translational research · 2026Review
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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.