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<p>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 <a href="https://compbio.sjtu.edu.cn/services/clc-db">https://compbio.sjtu.edu.cn/services/clc-db</a>, where all data are available for download.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月3日

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

摘要

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.