ReviewCell2024
De novo protein design-From new structures to programmable functions.
Review in Cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 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
150 citing papers in PubMed.
- Article
- Latent generative search unlocks de novo design of untapped biomolecular interactions at scale.bioRxiv : the preprint server for biology · 2026Article
- Sequence and structural determinants of efficacious de novo chimaeric antigen receptors.Nature biomedical engineering · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.Acta pharmacologica Sinica · 2026Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- De Novo-Designed Bifunctional Proteins for Targeted Protein Degradation.Journal of the American Chemical Society · 2026Article
- Dynamic view of an allosteric intermediate in a positively cooperative dimer.bioRxiv : the preprint server for biology · 2026Article
- Structure-forward targeting of claudins with synthetic binders.FEBS letters · 2026Review
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- Article
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- AbioRxiv : the preprint server for biology · 2026Article
- Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion.bioRxiv : the preprint server for biology · 2026Article
- Rapid plastid isolation reveals the chloroplast proteome and structures of the chlororibosome large subunit and RuBisCO inbioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Structure and evolution-guided design of minimal RNA-guided nucleases.Science (New York, N.Y.) · 2026Article
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets.bioRxiv : the preprint server for biology · 2026Article
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
90 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
1 author.
Funding
Abstract
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
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.