ArticleScientific reports2022
Machine learning-based analysis of overall stability constants of metal-ligand complexes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Review
- Mapping Steroidogenic Perturbations Under Endocrine Disruptor Mixtures Across Demographic Subgroups: Structural and Metabolomic Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Electronic Descriptors Governing Binding Energies of Bidentate Aromatic Ligands with Metal Cations: Insights from DFT and Statistical Analysis.The journal of physical chemistry. A · 2026Article
- A Comprehensive Machine Learning Model for Metal-Ligand Binding Prediction: Applications in Chemistry and Biology.Journal of chemical information and modeling · 2025Article
- Redefining Antifungal Treatment: The Innovation of Metal-Based Compounds.Journal of medicinal chemistry · 2025Review
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications.
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Registered trials
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