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Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

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Abstract

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (\({\Delta }_{fus}{G}_{A}^{\ominus }\)) and mixing the artificially liquid solute into the solvent (\({\Delta }_{m}{G}_{\left(A:B\right)}^{\ominus }\)). In this approach \({\Delta }_{fus}{G}_{A}^{\ominus }\) is predicted using machine learning models, and the \({\Delta }_{m}{G}_{\left(A:B\right)}^{\ominus }\) is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

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Acknowledgements

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Funding

The work was funded by Bayer AG.

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Authors and Affiliations

  1. Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096, Wuppertal, Germany

    Sadra Kashef Ol Gheta, Anne Bonin & Andreas H. Göller

  2. Bayer AG, Crop Science, R&D, Digital Transformation, 40789, Monheim, Germany

    Thomas Gerlach

  3. Bayer AG, Engineering & Technology, Thermal Separation Technologies, 51368, Leverkusen, Germany

    Thomas Gerlach

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  1. Sadra Kashef Ol Gheta
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  2. Anne Bonin
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  4. Andreas H. Göller
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Contributions

SK did the QM calculations, created the ML models and wrote the manuscript. AB prepared the datasets. TG and AG developed the concept and guided the work. All authors reviewed the manuscript.

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Correspondence to Andreas H. Göller.

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Gheta, S.K.O., Bonin, A., Gerlach, T. et al. Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state. J Comput Aided Mol Des 37, 765–789 (2023). https://doi.org/10.1007/s10822-023-00538-w

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