Evidence map›Paper›PMID 42080507›Full record

ArticleAngewandte Chemie (International ed. in English)2026

Machine Learning Accelerates Crystallization for Structure Determination.

Cui-Zhou Luan, Xue-Zhi Wang, Jian-Guo Song, Yu Gu, Jing Wu, Ye-Ting Wang, Jin-Feng Liang, Jia-Le Rao, Mo Xie, Jonathan R Nitschke and 1 more

Abstract read
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In one paragraph

Article in Angewandte Chemie (International ed. in English), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Machine Learning Accelerates Crystallization for Structure Determination.Angewandte Chemie (International ed. in English) · 2026
    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

11 authors.

Cui-Zhou LuanState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Xue-Zhi WangState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Jian-Guo SongState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Yu GuCollege of Physics and Optoelectronic Engineering, Jinan University, Guangzhou, P. R. China.
Jing WuState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Ye-Ting WangState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Jin-Feng LiangState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Jia-Le RaoState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.
Mo XieState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.ORCID https://orcid.org/0000-0001-6721-3711
Jonathan R NitschkeYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0002-4060-5122
Dan LiState Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry, Jinan University, Guangzhou, P. R. China.ORCID https://orcid.org/0000-0002-4936-4599

Funding

Guangdong Basic and Applied Basic Research Foundation 2024A1515011753Guangdong Major Project of Basic and Applied Research 2019B030302009Guangzhou Science and Technology Plan Project SL2024A04J00562National Natural Science Foundation of China 22431006National Natural Science Foundation of China 92461309Outstanding Innovative Talents Cultivation Funded Programs for Graduate Students of Jinan University 2025CXY321
6 · The paper itself

Abstract

Single-crystal X-ray diffraction (SCXRD) is a powerful tool for structural elucidation, but requires high-quality crystals that are often difficult to obtain. The crystalline mate strategy helps overcome this limitation by facilitating the co-crystallization of volatile or complex molecules, with few restrictions on size or purity. However, defining its scope of applicability remains challenging, until now requiring experimental trial-and-error screening. Here, we demonstrate a machine learning (ML)-accelerated workflow that rapidly identifies suitable candidates for co-crystallization. Through feature engineering and workflow optimization, we trained the MCC model, achieving over 95% prediction accuracy. Experimental validation confirmed 114 successful co-crystals among 120 predicted compounds. The wide structural and functional diversity exhibited highlights the robustness and broad applicability of our strategy, enabling efficient discovery of new structures by SCXRD under standard laboratory conditions.

Indexed as

co‐crystallizationcyclic trinuclear complexesmachine learningsingle‐crystal X‐ray diffractionstructure determination

Identifiers

PMID42080507
PMCPMC13266958

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