Virtual Multicomponent Crystal Screening: Hydrogen Bonding Revisited

Pharmaceutical cocrystals, salts, and multicomponent crystals, in general, have increasingly come under the spotlight in recent years. A fast and efficient a priori theoretical classifier to identify potential coformers is highly sought after to complement the experimental brute force screening methods. This research examines the qualitative approaches that are based on hydrogen bonding strength. First, molecular electrostatic potential (MEP) maps of 330 coformers were obtained from density functional theory simulations, using two geometries: experimentally determined crystal structures and gas-phase optimization. An in-depth comparison of MEPs revealed the potential pitfalls of these two geometries that are deliberated at length in the manuscript. Next, six APIs and their reported salts/cocrystals on the Cambridge Structural Database (CSD) were inversely predicted with MEP analysis. For two of these APIs, the prediction showed systematic errors that are resolved with suggestions provided in the manuscript. Subsequently, hydrogen bond energy (HBE) and hydrogen bond propensity (HBP) calculations were put to the test with two APIs and 52 organic coformers. Finally, multivariate logistic regression, a linear machine learning (ML) algorithm, showed how a combination of HBE and HBP can be a superior classifier, for which 18 out of 25 positive cases were uninterruptedly identified at the top of the list. Provided that a database of failed attempts of cocrystallization is compiled within the scientific community to supplement the existing positive results (multicomponent crystals in the CSD), the combination of chemistry-based parameters and ML can be a promising classifier for coformer selection.