Claim · 2020-01-15 · publication · Stated in source · Supported

Senior et al. (Nature, 15 January 2020) report that in CASP13 AlphaFold created high-accuracy structures, with template modelling scores of 0.7 or higher, for 24 out of 43 free modelling domains, whereas the next best method achieved such accuracy for only 14 out of 43 domains.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Improved protein structure prediction using potentials from deep learning → states provision → CASP13 free-modelling result as the authors report it

Where it appears

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Claim and evidence

Improved protein structure prediction using potentials from deep learningstates provision ↓CASP13 free-modelling result as the authors report it

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Provision: Statement · provision ID mod-af-casp13-domains
Date: 2020-01-15 · publication Records dated 2020 →

Scope and limitsThe authors' report of their own CASP13 entry, published after the assessment; the CASP organisers' statements about CASP13, in their overview of the round, are recorded separately. Comparison is to the next best method in that round on that domain set only.

The evidence 01

primary sourceSupports the claim

Improved protein structure prediction using potentials from deep learning

Nature · 2020-01-15

Cite the original source

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Source excerpt

“AlphaFold created high-accuracy structures … for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains.”

Location in source abstract

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-30

Source details and check notes

Authors: Andrew W. Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Žídek; Alexander W. R. Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; Karen Simonyan; Steve Crossan; Pushmeet Kohli; David T. Jones; David Silver; Koray Kavukcuoglu; Demis Hassabis
Version: Retrieved for the candidate pack on 2026-10-30 (SHA-256 ae72fc1c6b140ff6c74fce998cd8f44d02cacbb78e381988b8ed8e0d517ebf2a). Nature article page, as served on 2026-10-30; author list and Author information section. The candidate pack retrieved the same page a second time on 2026-10-30, at an address carrying a cookie-error query string (SHA-256 0a0093d14f221edfb91d3a7bb52aa5ae5b1899935c1ccc63c86dc2e7f36c5f1d); the passages read in that retrieval are cited here.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-30
Language: English (en)
Attribution: Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis, “Improved protein structure prediction using potentials from deep learning”, 2020-01-15.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (33 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. The ellipsis replaces the parenthesis "(with template modelling (TM) scores6 of 0.7 or higher)", which carries a reference marker in the page text. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. An exact framework edition, or another document with an exact expression, states one provision: a capability threshold or risk level and its trigger, a required safeguard or action, a decision authority (who decides, recommends, can overrule or is informed), a stated exception, or another statement of its content, such as a licence term or a prospectus statement. The passage records what the document says, not implementation, compliance or effectiveness. Provisions with the same key align across editions of one family.

Date. Date of the paper reporting the result.

Record ID aib-mod-af-casp13-domains