Abstract
Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.
Highlights
Active pharmaceutical ingredients (APIs) are commonly formulated and delivered to patients in the solid dosage forms for reasons of economy, stability, and convenience of intake [1]
Coformers for pharmaceutical cocrystallization should be from the Food and Drug Administration (FDA)’s list of Everything Added to Food in the United States (EAFUS) or from the Generally Recognized as Safe list (GRAS), as they should have no adverse or pharmacological toxic effects [1]
We might say that if we want efficiency, the Recursive Feature Elimination (RFE) algorithm will be preferable; on the other hand, the K-best algorithm is preferable if we want effectiveness
Summary
Active pharmaceutical ingredients (APIs) are commonly formulated and delivered to patients in the solid dosage forms (tablets, capsules, powders) for reasons of economy, stability, and convenience of intake [1]. One of the major problems faced during the formulation of drug is its low bioavailability which is mainly reliant on the solubility and permeability of API [2,3], and one of the approaches to enhance the physicochemical and pharmacological properties of API without modifying its intrinsic chemical structure is to develop novel solid forms such as cocrystals [4,5,6,7]. Pharmaceutical cocrystals, a subclass of cocrystals, are stoichiometric molecular complexes composed of APIs and pharmaceutically acceptable coformers held together by non-covalent interactions such as hydrogen bonding within the same crystal lattice [14]. Since experimental determination of cocrystals is time-consuming, costly, and labor-intensive, it is valuable to develop complementary tools that can reduce the list of coformers by predicting which coformers are likely to form cocrystals [16]
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