ArticleACS omega2022
Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluating the Effectiveness of Reference Solvent Solubility Calculations for Binary Mixtures Based on Pure Solvent Solubility: The Case of Phenolic Acids.Molecules (Basel, Switzerland) · 2025Article
- Multi-omics pan-cancer profiling of CDK2 andRSC advances · 2025Article
- A unified approach to inferring chemical compounds with the desired aqueous solubility.Journal of cheminformatics · 2025Article
- Identification of acetylcholinesterase inhibitors from traditional medicinal plants for Alzheimer's disease usingRSC advances · 2024Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design.ACS omega · 2024Article
- Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine.BMC chemistry · 2024Article
- Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.Molecular pharmaceutics · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
Aqueous solubility is one of the most important physicochemical properties in drug discovery. At present, the prediction of aqueous solubility of compounds is still a challenging problem. Machine learning has shown great potential in solubility prediction. Most machine learning models largely rely on the setting of hyperparameters, and their performance can be improved by setting the hyperparameters in a better way. In this paper, we used MACCS fingerprints to represent the structural features and optimized the hyperparameters of the light gradient boosting machine (LightGBM) with the cuckoo search algorithm (CS). Based on the above representation and optimization, the CS-LightGBM model was established to predict the aqueous solubility of 2446 organic compounds and the obtained prediction results were compared with those obtained with the other six different machine learning models (RF, GBDT, XGBoost, LightGBM, SVR, and BO-LightGBM). The comparison results showed that the CS-LightGBM model had a better prediction performance than the other six different models. RMSE, MAE, and
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