Evidence map›Paper›PMID 40549238›Full record

ReviewScience China. Life sciences2025

AI4Protein: transforming the future of protein design.

Dequan Wang, Zheling Tan, Jin Gao, Shaoting Zhang, Jiaqi Shen, Yuming Lu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Science China. Life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026
    Review
  5. Article
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

6 authors.

Dequan Wang *School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zheling Tan *School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jin Gao *School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shaoting ZhangSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jiaqi ShenSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yuming LuSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China. luymin@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has revolutionized the protein engineering process from multiple aspects, including representing protein information, generating protein designs, and evaluating protein properties. This review aims to introduce the recent progress of AI in protein research. We first introduce how AI models represent protein sequences, structures, and other properties. Further, the applications of generative models in protein design are introduced. The use of predictive models and smart agents in the evaluation process is then discussed, including high-precision protein property simulation and wet lab experimental design. Additionally, we discuss the future development of AI in protein research and the potential challenges it may encounter.

Indexed as

Artificial IntelligenceProtein EngineeringProteinsHumansModels, MolecularProtein ConformationProteinsartificial intelligenceprotein designprotein engineering

Identifiers

PMID40549238

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.