Publication Frequency Changed
2025-01-14
Enhancing Environmental Sustainability through Machine Learning: Predicting Drug Solubility (LogS) for Ecotoxicity Assessment and Green Pharmaceutical Design
Authors
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Imane Aitouhanni
SSLAB, ENSIAS, Mohammed V University, Rabat 10000, Morocco
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Amine Berqia
SSLAB, ENSIAS, Mohammed V University, Rabat 10000, Morocco
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Redouane Kaiss
Research Laboratory in Economics, Management, and Business Administration, Faculty of Economics and Management, Hassan 1st University, Settat 26000, Morocco
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Habiba Bouijij
SSLAB, ENSIAS, Mohammed V University, Rabat 10000, Morocco
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Yassine Mouniane
Natural Resources and Sustainable Development laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra 14000, Morocco
DOI:
https://doi.org/10.30564/jees.v7i4.8866Abstract
Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down, and the drug products are accumulated in living organisms. The ability of a drug to dissolve in water (i.e., LogS) is an important parameter for assessing a drug's environmental fate, biovailability, and toxicity. LogS is typically measured in a laboratory setting, which can be costly and time-consuming, and does not provide the opportunity to conduct large-scale analyses. This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants. We used a dataset from the ChEMBL database that contained 8832 molecular compounds. Various data preprocessing and cleaning techniques were applied (i.e., removing the missing values), we then recorded chemical properties by normalizing and, even, using some feature selection techniques. We evaluated logS with a total of several machine learning and deep learning models, including; linear regression, random forests (RF), support vector machines (SVM), gradient boosting (GBM), and artificial neural networks (ANNs). We assessed model performance using a series of metrics, including root mean square error (RMSE) and mean absolute error (MAE), as well as the coefficient of determination (R²). The findings show that the Least Angle Regression (LAR) model performed the best with an R² value close to 1.0000, confirming high predictive accuracy. The OMP model performed well with good accuracy (R² = 0.8727) while remaining computationally cheap, while other models (e.g., neural networks, random forests) performed well but were too computationally expensive. Finally, to assess the robustness of the results, an error analysis indicated that residuals were evenly distributed around zero, confirming the results from the LAR model. The current research illustrates the potential of AI in anticipating drug solubility, providing support for green pharmaceutical design and environmental risk assessment. Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
Keywords:
Solubility; Prediction; Machine Learning; Ecotoxicity; LogSReferences
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Aitouhanni, I., Berqia, A., Kaiss, R., Habiba Bouijij, & Mouniane, Y. (2025). Enhancing Environmental Sustainability through Machine Learning: Predicting Drug Solubility (LogS) for Ecotoxicity Assessment and Green Pharmaceutical Design. Journal of Environmental & Earth Sciences, 7(4), 82–95. https://doi.org/10.30564/jees.v7i4.8866
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Copyright © 2025 Imane Aitouhanni, Amine Berqia, Redouane Kaiss, Habiba Bouijij, Yassine Mouniane
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
