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We report a single machine learning (ML)-based model to predict the solubility of drug/drug-like compounds across 49 organic solvents, extensible to more. By adopting a cross-solvent data structure, we enable...

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This study presents a modelling framework to predict the flowability of various commonly used pharmaceutical powders. The flowability models were trained and validated on 86 samples including single components and binary mixtures. Two modelling paradigms based on artificial intelligence (AI) namely, a radial basis function (RBF) and an integrated network were employed to model the flowability represented by the flow function coefficient (FFC) and the bulk density (RHOB). Both approaches were utilized to map the input parameters (i.e. particle size, shape descriptors and various materials and mixtures) to the flow properties. The input parameters of the blends were determined from the particle size and shape properties of the single components. The results clearly indicated that the integrated network outperformed the single RBF network in terms of the predictive performance and the generalization capabilities. For the integrated network, the coefficient of determination of the testing data set (not used for training the model) for 2 FFC was 2 = 0.93, reflecting an acceptable predictive power of this model. Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their concentrations to improve flowability with minimal experimental effort and material. The presented modelling approach can thus be employed instead of actual measurements throughout the process development stage resulting in the (i) minimization of the time required, (ii) exploration and examination of the design space, and (iii) minimization of material waste.