Evidence map›Paper›PMID 42237181›Full record

ArticleJournal of cheminformatics2026

Integrating multimodal features with deep learning for protein solubility prediction.

Zechen Wang, Lai Heng Tan, Liangzhen Zheng, Jagath C Rajapakse, Yuguang Mu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Zechen WangCollege of Computing and Data Science, Nanyang Technological University, Singapore, 639798, Singapore.
Lai Heng TanSchool of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore.
Liangzhen ZhengShanghai Zelixir Biotech Co. Ltd.,, Shanghai, 200030, China. zhenglz@zelixir.com.
Jagath C RajapakseCollege of Computing and Data Science, Nanyang Technological University, Singapore, 639798, Singapore. asJagath@ntu.edu.sg.
Yuguang MuSchool of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore. ygmu@ntu.edu.sg.

Funding

Ministry of Education - Singapore AcRF Tier-2 grant MOE-T2EP20224-0004National Key R& D Program of China 2023YFA0915500
6 · The paper itself

Abstract

Protein solubility prediction holds significant importance in the fields of biotechnology and medicine. With the continual advancements of computational and experimental techniques such as protein design, enzyme mining, and directed evolution, accurate prediction of native and mutant protein solubility has become a key step in accelerating the development of functional proteins. In this study, we extracted physicochemical properties and co-evolutionary features from protein sequences, and further incorporated graph-based protein representations along with surface features as inputs for solubility prediction. Building upon these features, we developed two models, ProSolNet and ProSolNet

Indexed as

Deep learningGraph neural networkMultimodal featuresProtein solubility

Identifiers

PMID42237181
PMCPMC13449401

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.