AlphaFold
Google DeepMind's protein-structure prediction AI — the Nobel-winning tool that mapped the shape of nearly every known protein.
Overview
AlphaFold predicts the 3D structure of proteins from their amino-acid sequence, a decades-old grand challenge in biology. Its creators, Demis Hassabis and John Jumper, shared the 2024 Nobel Prize in Chemistry. AlphaFold 3 (2024) extends predictions to DNA, RNA, ligands and other biomolecules, aiding drug discovery. The free AlphaFold Server offers non-commercial AF3 predictions, and the open AlphaFold Protein Structure Database holds 200M+ structures used by researchers worldwide.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 24 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
AlphaFold in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
What this is for: Predicting the 3D structure of proteins and their complexes with DNA, RNA and small molecules, accelerating structural biology and drug discovery.
Who this is for: Academic and non-commercial researchers in biology, chemistry and medicine.
Availability: Free AlphaFold Server for non-commercial research, plus an open structure database; AF3 model code is released for non-commercial use only.
What people say
AlphaFold's reception has been about as strong as it gets in science. After its 2020 CASP results, organisers declared a 50-year grand challenge effectively solved; in 2024, John Jumper and Demis Hassabis shared the Nobel Prize in Chemistry (with David Baker) for the work. The AlphaFold Protein Structure Database is now used by more than two million researchers across roughly 190 countries, making the tool a routine part of structural-biology and drug-discovery workflows worldwide.
The caveats, however, are widely discussed by practitioners. AlphaFold predicts a single static structure, so it struggles with intrinsically disordered regions, alternative conformations and the effects of point mutations — recent benchmarks such as DISPROTBENCH specifically probe where it fails on disordered proteins. Predicted confidence (pLDDT) is not the same as experimental validation, and it does not model ligands, dynamics or many complexes the way experiments do.
There is also a sociological critique: some scholars describe the AlphaFold story as a "solutionist," contest-driven narrative, and note that AlphaFold 3's initially code-restricted, non-commercial release drew pushback from researchers who wanted fully open, reproducible tooling. The consensus is that it is transformative but a hypothesis generator — its structures still need wet-lab confirmation.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including AlphaFold's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with AlphaFold unless explicitly stated.
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