ArticleMolecular pharmaceutics2026
Combining PC-SAFT and ML to Access Unknown API Solubilities.
Article in Molecular pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Predicting the solubility of active pharmaceutical ingredients (APIs) is essential throughout drug development. However, state-of-the-art modeling approaches require system-specific data sets for parameter estimation and are resource intensive. This work introduces a new method that integrates adaptive machine learning (ML) methods with PC-SAFT modeling to vastly reduce requirements of experimental data. Instead of extensive experimental campaigns of solubility measurements, only the molecular structure and melting properties of the API are needed - information onten available in literature or easily measured. The ML framework applied in this work supplies PC-SAFT parameters for APIs. With solvent parameters already available from literature, this novel approach provided highly accurate solubility estimations for 21 APIs in pure solvents (
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