Evidence map›Paper›PMID 40181386›Full record

ArticleJournal of cheminformatics2025

Clc-db: an open-source online database of chiral ligands and catalysts.

Gufeng Yu, Kaiwen Yu, Xi Wang, Chenxi Zhang, Yicong Luo, Xiaohong Huo, Yang Yang

Abstract read
In one paragraph

Article in Journal of cheminformatics, 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
  2. 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.

Gufeng Yu *Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Kaiwen Yu *Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Xi Wang *Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Chenxi ZhangShanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yicong LuoShanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Xiaohong HuoShanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. huoxiaohong@sjtu.edu.cn.
Yang YangDepartment of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. yangyang@cs.sjtu.edu.cn.

Funding

National Key Research and Development Program of China No. 2018YFE0126800National Key Research and Development Program of China No. 2023YFC2811500National Natural Science Foundation of China Nos. 21831005National Natural Science Foundation of China Nos. 62272300
6 · The paper itself

Abstract

The design and optimization of chiral ligands and catalysts are fundamental to advancing asymmetric catalysis, a critical area in organic chemistry with wide-ranging impacts across scientific disciplines. Traditional experimental approaches, while essential, are often hindered by their slow pace and complexity. Recent advancements have demonstrated that computational methods, particularly machine learning, offer transformative potential by significantly accelerating these processes through enhanced prediction and modeling capabilities. However, limitations such as data scarcity and model inaccuracies continue to challenge their broader application. To address these issues, we present the Chiral Ligand and Catalyst Database (CLC-DB), the first open-source, comprehensive database specifically designed for chiral ligands and catalysts. CLC-DB contains 1,861 molecules spanning 32 distinctive chiral ligand and catalyst categories, with each entry annotated with 34 types of curated information, validated by chemical experts and linked to authoritative chemical databases. The database features a user-friendly interface that supports efficient single and batch searches, as well as an integrated, high-performance online molecular clustering tool to facilitate computational analyses. CLC-DB is freely accessible at https://compbio.sjtu.edu.cn/services/clc-db , where all data are available for download.

Indexed as

Chiral catalystChiral catalyst designChiral ligandOpen-source database

Identifiers

PMID40181386
PMCPMC11969900

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.