ReviewNature structural & molecular biology2026
Computation and deep-learning-driven advances in CRISPR genome editing.
Review in Nature structural & molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Atomistic Simulations Decode the Mechanisms of DNA and RNA Processing Enzymes: Function through Motion.Accounts of chemical research · 2026Article
- Molecular mechanisms and biotechnology applications of CRISPR-Cas12a.Nature reviews. Molecular cell biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome editing with CRISPR-Cas systems is revolutionizing medicine, molecular biology and biotechnology. In this Review, we discuss the contributions of deep learning-based structure prediction algorithms, physics-based simulations, neural networks, graph neural networks and generative models, including diffusion and large language models, in engineering and optimizing CRISPR systems and in understanding their mechanistic basis. We highlight the challenges and limitations to the transformative effects of computational modeling and tools in the context of the development of programmable genome editors for biomedicine and biotechnology.
Indexed as
Identifiers
41699287What 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.