Accelerating Rational Crystal Habit Design via Interpretable Mechanism-Guided Machine Learning Framework

The pharmaceutical crystal habit significantly influences downstream processing and product quality. However, traditional experimental screening methods rely heavily on empirical knowledge and trial-and-error experimentation, demanding substantial resources and time. Here we present an interpretable machine learning framework integrating molecular descriptors with quantum chemical (QC) or thermodynamic (HSP, PC-SAFT) parameters to predict crystal habits. Using a curated database of 418 entries covering 153 APIs and 41 solvents, the XGBoost model demonstrated superior performance among seven algorithms. Models augmented with QC or thermodynamic descriptors significantly improved prediction, achieving AUC > 0.88. SHAP analysis revealed dipole moments and solubility parameter differences as key determinants of crystal habit, enhancing interpretability. External validation on six compounds in seven solvents confirmed that models with additional mechanistic descriptors consistently outperformed the molecular-only baseline, with the Molecular+QC model showing the best overall performance. This proposal paves the way to the rational design of crystal engineering strategies for habit prediction and control, and offers a general framework that can readily be extended to other crystallographic applications, as well as broader domains, including drug discovery and materials science.