What question did this study set out to answer?

The study aims to create an interpretable machine learning framework to predict the solubility of drug-like compounds in aqueous and organic solvents.

May 16, 2026ACS OmegaOpen Access

Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches

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Authors

BGBoinapalli GopichandAmrita Vishwa Vidyapeetham GNGM NairAmrita Vishwa Vidyapeetham BNB Amba NairAmrita Vishwa Vidyapeetham

Discussion

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Overview

Randomized trial demonstrates enhanced solubility prediction in drug-like compounds, highlighting molecular and solvent effects.

Key Points

The study aims to create an interpretable machine learning framework to predict the solubility of drug-like compounds in aqueous and organic solvents.
Developed and validated interpretable machine learning models using AqSolDB, AqSolDBc, BigSolDB, and BigSolDB 2.0 datasets.
Applied 5-fold cross-validation with scaffold-based and solute–solvent pair splitting for model training.
Conducted feature selection and hyperparameter tuning to optimize model performance.
Optimized models significantly improved performance metrics compared to baseline configurations.
Aqueous solubility prediction was primarily governed by polarity and hydrogen-bonding descriptors, while organic solubility was influenced by solvent characteristics and molecular topology.
Prediction error increased with structural dissimilarity from training set, indicating robustness and transferability of the model.

Cite This Study

Gopichand et al. (2026) studied this question.

synapsesocial.com/papers/6a08093ca487c87a6a40b217 https://doi.org/10.1021/acsomega.5c13630

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May 16, 2026ACS OmegaOpen Access

Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches

View Full Paper

Ask AI

Bookmark

Authors

BGBoinapalli GopichandAmrita Vishwa Vidyapeetham GNGM NairAmrita Vishwa Vidyapeetham BNB Amba NairAmrita Vishwa Vidyapeetham

Discussion

Member takes

Overview

Randomized trial demonstrates enhanced solubility prediction in drug-like compounds, highlighting molecular and solvent effects.

Key Points

The study aims to create an interpretable machine learning framework to predict the solubility of drug-like compounds in aqueous and organic solvents.
Developed and validated interpretable machine learning models using AqSolDB, AqSolDBc, BigSolDB, and BigSolDB 2.0 datasets.
Applied 5-fold cross-validation with scaffold-based and solute–solvent pair splitting for model training.
Conducted feature selection and hyperparameter tuning to optimize model performance.
Optimized models significantly improved performance metrics compared to baseline configurations.
Aqueous solubility prediction was primarily governed by polarity and hydrogen-bonding descriptors, while organic solubility was influenced by solvent characteristics and molecular topology.
Prediction error increased with structural dissimilarity from training set, indicating robustness and transferability of the model.

Cite This Study

Gopichand et al. (2026) studied this question.

synapsesocial.com/papers/6a08093ca487c87a6a40b217 https://doi.org/10.1021/acsomega.5c13630

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