Cocrystal Formation Prediction: Hybrid GIN-Mordred Model Outperforms DFT-Based Methods

Cocrystals offer significant potential across various
industries,
especially pharmaceuticals, by addressing the poor solubility of new
drug candidates. However, traditional experimental screening for cocrystal
formation is expensive and time-consuming, highlighting the need for
predictive models. In this study, we compared four cocrystal prediction
approaches: two deep learning (DL) models based on DFT-driven data
(PointNet for electrostatic potential (ESP) maps and a novel LSTM
for sequential hydrogen bond parameters), a novel hybrid model combining
graph isomorphism networks (GIN) with Mordred descriptors, and the
empirical Hydrogen Bond Energy (HBE) method. To perform this comparison,
we compiled and carried out DFT calculations for 14,790 molecules
(7395 pairs of successful and unsuccessful cocrystals). Notably, the
GIN-Mordred model outperformed all other methods, achieving the highest
balanced accuracy (BACC: 0.916), F1-score (0.956), recall (0.932),
and AUC (0.97), with superior segregation performance in distinguishing
between cocrystallization outcomes. Importantly, the GIN-Mordred model
does not require costly DFT calculations, demonstrating that a combination
of graph-based and descriptor-based molecular representation provides
an efficient and accurate alternative for cocrystal prediction. This
model significantly streamlines the process of tuning the physicochemical
properties of crystalline materials for various applications.