Cocrystal Formation Prediction: Hybrid GIN-Mordred Model Outperforms DFT-Based Methods
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".