ReviewCureus2024
Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed.
- Systematic prioritization of candidate genes in camptothecin biosynthesis using multi-omics and deep learning.The plant genome · 2026Article
- AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.International journal of molecular sciences · 2026Article
- Advances in Biosimilars: A Systematic Review of Machine Learning Applications.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.Pharmaceutics · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Brief Progress in Methods for Deciphering Protein-Protein Interaction Networks.International journal of molecular sciences · 2026Review
- Acquisition of amantadine resistance via M gene reassortment in canine H3N2 influenza virus and elucidation of the resistance mechanism.Virology journal · 2026Article
- Evaluation of AI-Predicted GH11 Xylanase Models Against a Previously Unreported Experimental Structure: Implications for Conformational Accuracy and Ligand Binding.International journal of molecular sciences · 2026Article
- Industry-defined opportunities for advancing big data capabilities in biopharmaceutical manufacturing.Frontiers in bioengineering and biotechnology · 2026Review
- Animal virus-host interactions mediated by non-coding RNAs.Frontiers in cellular and infection microbiology · 2026Review
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Phytochemical Profiling and Anticancer Potential ofAnti-cancer agents in medicinal chemistry · 2026Article
- Enhanced Sampling and Free Energy Calculations in Protein Simulations.Advances in experimental medicine and biology · 2026Review
- Structural Studies on the M. tuberculosis Nucleoid-associated-Protein, NapA, Indicates DNA Bridging Mechanism.Journal of molecular biology · 2025Article
- Neurofibromatosis Type 1 and the Search for Effective Tumor Therapies Using High-Throughput Drug Screening.Current oncology (Toronto, Ont.) · 2025Review
- Predicted peptide scaffolds for drug screening in endometrial cancer organoids.Scientific reports · 2025Article
- Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.Planta · 2025Review
- Integrative structural profiling and ligand optimisation across the transthyretin mutational landscape.NPJ systems biology and applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.
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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.