Comparative analysis of chemical descriptors by machine learning reveals atomistic insights into solute–lipid interactions
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Publication date
23.05.2024
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01A - Journal article
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Molecular Pharmaceutics
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21
Issue / Number
7
Pages / Duration
3343–3355
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American Chemical Society
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Abstract
This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.
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1543-8384
1543-8392
1543-8392
Language
English
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Yes
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Published
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Peer review of the complete publication
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Hybrid
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Citation
Lange, J. J., Anelli, A., Alsenz, J., Kuentz, M., O’Dwyer, P. J., Saal, W., Wyttenbach, N., & Griffin, B. T. (2024). Comparative analysis of chemical descriptors by machine learning reveals atomistic insights into solute–lipid interactions. Molecular Pharmaceutics, 21(7), 3343–3355. https://doi.org/10.1021/acs.molpharmaceut.4c00080