Evidence mapPaperPMID 36918529Full record

ReviewSignal transduction and targeted therapy2023

AlphaFold2 and its applications in the fields of biology and medicine.

Zhenyu Yang, Xiaoxi Zeng, Yi Zhao, Runsheng Chen

Open access · goldFull text readReview
In one paragraph

Review in Signal transduction and targeted therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 268 papers.

0numbers the graph read from it
0cells of the map it votes in
268citing papers in PubMed
81.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

268 citing papers in PubMed, 513 citations in OpenAlex.

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208 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Zhenyu YangWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Xiaoxi ZengWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China. zengxiaoxi@wchscu.cn.
Yi ZhaoWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China. biozy@ict.ac.cn.ORCID 0000-0001-6046-8420
Runsheng ChenWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China. crs@ibp.ac.cn.
Sichuan University · CNChinese Academy of Sciences · CNWest China Medical Center of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Indexed as

Artificial IntelligenceFurylfuramideAmino Acid SequenceBiologyProteinsFurylfuramideProteins

Identifiers

PMID36918529
PMCPMC10011802
OpenAlexW4324143928

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read22
Read underepoch 390

Registered trials

None linked

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 Socratic graph.