ReviewDrug discovery today2023
AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.
Review in Drug discovery today, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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
50 citing papers in PubMed.
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- Hybrid Computational Modeling with Multi-Level Validation Identifies TK1-VIM as a Robust Therapeutic Pair in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Stabilizing Plasmodium falciparum proteins for small molecule drug discovery.Protein science : a publication of the Protein Society · 2026Article
- Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling.Journal of chemical information and modeling · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Fine-Tuning DiffDock-L for Allosteric Kinase Docking.Journal of chemical information and modeling · 2026Article
- Protein structural dynamics in covalent drug design: insights from irreversible and reversible covalent inhibitors.RSC chemical biology · 2026Review
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- The non-catalytic binding of auxin, cytokinin and salicylic acid by tomato Phi class glutathione transferases: insights from computational modelling.Plant molecular biology · 2026Article
- Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Support field neural representation learner framework for learning stability landscapes in molecular geometry.npj biomedical innovations · 2025Article
- Undirected Exploration of Binding Pockets with Flexible Topology.Journal of chemical theory and computation · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge.Journal of molecular biology · 2025Review
- Isoliquiritigenin targets las, rhl, and Pqs quorum sensing systems to mitigate the virulence and infection of Pseudomonas aeruginosa.BMC microbiology · 2025Article
- Accurate Protein Dynamic Conformational Ensembles: Combining AlphaFold, MD, and AmideInternational journal of molecular sciences · 2025Article
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer's Disease and Other Neurological Disorders.Pharmaceuticals (Basel, Switzerland) · 2025Review
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
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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.