ReviewThe journal of physical chemistry. B2022
AlphaFold, Artificial Intelligence (AI), and Allostery.
Review in The journal of physical chemistry. B, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 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
90 citing papers in PubMed.
- The Allosteric Revolution: From Static Structures to Conformational Ensembles and Next-Generation Therapeutics.Journal of molecular biology · 2026Review
- Critical review of artificial intelligence in synthetic biology: DBTL cycle applications, challenges, and design rules for microbial strain engineering.World journal of microbiology & biotechnology · 2026Review
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Construction of a Neoantigen Prognostic Model for Gastric Adenocarcinoma Based on Multi-Omics Data Mining and the Design of mRNA Vaccines and Targeted Drugs.International journal of molecular sciences · 2026Article
- Structure-Guided Immunoinformatics for the Rational Design of a Multi-Epitope Vaccine Against Batai Orthobunyavirus.Pharmaceuticals (Basel, Switzerland) · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- Immunopharmacological design of a multi-epitope vaccine targeting sarbecovirus Spike glycoprotein.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Immunopharmacologsical and structural insights into Rift Valley fever virus envelope glycoproteins: an immunoinformatics approach to multi-epitope vaccine design.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Decoding the allosteric grammar of protein kinases: A dual-stream framework integrating protein language models and energy landscape frustration analysis.Protein science : a publication of the Protein Society · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Genome-Guided Discovery of Vaccine Targets for a Multi-Epitope Construct Against Multidrug-Resistant Enterobacter cloacae.MicrobiologyOpen · 2026Article
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Engineering Compact Base Editors by AlphaFold-Guided Mutation Scan and Escherichia coli-Based Tri-Selection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TaLiRAGen: target-aware ligand generation via retrieval-augmented large language models.Molecular diversity · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The Evolving Role of Artificial Intelligence in Andrological Surgery: Current Landscape and Future Direction.Journal of clinical medicine · 2026Review
30 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
4 authors.
Funding
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
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.