Evidence map›Paper›PMID 39080777›Full record

ArticleJournal of cheminformatics2024

Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data.

Chengwei Zhang, Yushuang Zhai, Ziyang Gong, Hongliang Duan, Yuan-Bin She, Yun-Fang Yang, An Su

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  4. 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

7 authors.

Chengwei ZhangState Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
Yushuang ZhaiState Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
Ziyang GongKey Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China.
Hongliang DuanFaculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
Yuan-Bin SheState Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
Yun-Fang YangState Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
An SuState Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China. ansu@zjut.edu.cn.ORCID http://orcid.org/0000-0002-6544-3959

Funding

Joint Funds of the Zhejiang Provincial Natural Science Foundation of China LHDMZ23B060001Key Research and Development Program of Zhejiang Province 2022C01179National Natural Science Foundation of China 22108252
6 · The paper itself

Abstract

Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R

Indexed as

Chemical reaction databaseMachine learningOrganic materialsTransfer learningVirtual screening

Identifiers

PMID39080777
PMCPMC11290278

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.