Claim · 2021-04-05 · publication · Stated in source · Supported

Rives et al. (PNAS, 5 April 2021) state that protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology.

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What it connects

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences → states provision → Protein language modelling framed as a step towards predictive and generative AI for biology

Where it appears

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Claim and evidence

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesstates provision ↓Protein language modelling framed as a step towards predictive and generative AI for biology

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Provision: Statement · provision ID mod-esm1b-framing
Date: 2021-04-05 · publication Records dated 2021 →

Scope and limitsA framing statement by the authors about their research direction, not a finding.

The evidence 01

primary sourceSupports the claim

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Proceedings of the National Academy of Sciences (open access copy in PubMed Central) · 2021-04-05

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Source excerpt

“Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology.”

Location in source abstract

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-30

Source details and check notes

Authors: Alexander Rives; Joshua Meier; Tom Sercu; Siddharth Goyal; Zeming Lin; Jason Liu; Demi Guo; Myle Ott; C. Lawrence Zitnick; Jerry Ma; Rob Fergus
Version: Retrieved for the candidate pack on 2026-10-30 (SHA-256 5a365c9b9bca1d0cb6065822636366ef838a8192d09787229202ea2c5628b236). PMC8053943 copy of PNAS 118(15):e2016239118.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-30
Language: English (en)
Attribution: Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus, “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”, 2021-04-05.
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 (20 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. No human editorial review.

Status and meaning

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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 making the statement.

Record ID aib-mod-esm1b-framing