Evidence map›Paper›PMID 39929993›Full record

ArticleCommunications chemistry2025

An active representation learning method for reaction yield prediction with small-scale data.

Peng-Xiang Hua, Zhen Huang, Zhe-Yuan Xu, Qiang Zhao, Chen-Yang Ye, Yi-Feng Wang, Yun-He Xu, Yao Fu, Hu Ding

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Peng-Xiang Hua *School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Zhen Huang *School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Zhe-Yuan Xu *Key Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Qiang ZhaoKey Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Chen-Yang YeKey Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Yi-Feng WangKey Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China. yfwangzj@ustc.edu.cn.ORCID http://orcid.org/0000-0003-1360-8931
Yun-He XuKey Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China. xyh0709@ustc.edu.cn.ORCID http://orcid.org/0000-0001-8817-0626
Yao FuKey Laboratory of Precision and Intelligent Chemistry, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China. fuyao@ustc.edu.cn.ORCID http://orcid.org/0000-0003-2282-4839
Hu DingSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230026, China. huding@ustc.edu.cn.ORCID http://orcid.org/0000-0002-1307-6077

Funding

Natural Science Foundation of Anhui Province (Anhui Provincial Natural Science Foundation) 2208085MF163
6 · The paper itself

Abstract

Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called "RS-Coreset", where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.

Identifiers

PMID39929993
PMCPMC11811124

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