Verified claim · AI-ML · 100% confidence
AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction.
Last verified 2026-05-16 · Methodology veritas-v0.1 · a77a8dd48941a53d
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
AlphaFold 3 released on: 2024-05-08.
100% confidence · shares 4 tags (alphafold, deepmind, protein-structure…)
AlphaFold 2 published in paper: Highly accurate protein structure prediction with AlphaFold (Jumper et al., 2021).
100% confidence · shares 3 tags (alphafold, deepmind, nature)
Backpropagation algorithm popularized in: Rumelhart, Hinton, Williams 1986 — Nature paper.
100% confidence · shares 3 tags (foundational, introduced_in, nature)
ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval.
100% confidence · shares 3 tags (foundational, 2020, introduced_in)
Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models.
100% confidence · shares 3 tags (foundational, 2020, introduced_in)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
100% confidence · shares 2 tags (foundational, 2020)
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
100% confidence · shares 2 tags (foundational, deepmind)
Vision Transformer (ViT) introduced in paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Dosovitskiy et al., 2020).
100% confidence · shares 2 tags (foundational, 2020)
Structured fields
- Subject
- AlphaFold 1
- Predicate
introduced_in- Object
- Senior et al. 2020 — DeepMind protein structure prediction
- Confidence
- 100%
- Tags
- alphafold-1 · alphafold · deepmind · protein-structure · foundational · 2020 · introduced_in · nature
Sources (2)
[1] peer reviewed · Nature (Senior, Evans, Jumper, et al. / DeepMind) · 2020-01-15
Improved protein structure prediction using potentials from deep learning“Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function.”
[2] official blog · Google DeepMind · 2020-01-15
AlphaFold: Using AI for scientific discoveryGoogle DeepMind is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction. — SourceScore Claim a77a8dd48941a53d (verified 2026-05-16). https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/a77a8dd48941a53d/" width="100%" height="360" frameborder="0" loading="lazy" title="AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction."?
The record lists 2 cited source(s): Nature (Senior, Evans, Jumper, et al. / DeepMind), Google DeepMind. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "AlphaFold 1 introduced in: Senior et al. 2020 — DeepMind protein structure prediction."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_alphafold_1_fact() -> dict:
"""Fetch the verified SourceScore claim for AlphaFold 1."""
r = httpx.get("https://sourcescore.org/api/v1/claims/a77a8dd48941a53d.json")
return r.json()