Machine Learning-Guided Prediction of Cocrystals Using Point Cloud-Based Molecular Representation
Bibliographic record
Abstract
The design and synthesis of cocrystals have emerged as promising crystal engineering strategies for enhancing the physicochemical properties of a diverse range of target molecules. A prediction strategy to identify whether a pair of target and auxiliary molecules would form a cocrystal can greatly accelerate the process of cocrystal discovery. In this study, we compiled and performed DFT calculations for 12,776 molecules (6,388 cocrystals). All entries in the database were obtained from experimental attempts reported in the literature. Electrostatic potential (ESP) surfaces were then extracted from the DFT results and used for the development of four machine learning models (PointNet, ANN, RF, Ensemble). The Ensemble model, leveraging the complementary strengths of the PointNet, ANN, and RF models, demonstrated superior discriminatory performance with a BACC (0.942) and an AUC (0.986) on the unseen test data subset. To assess the performance of the models on individual molecules, we separated the cocrystals of caffeine, fumaric acid, and salicylic acid from the overall database. The Ensemble model exhibited remarkable robustness, classifying the 312 cocrystals in this subset into their respective classes, with an average BACC of 98%. Furthermore, through conducting data analysis, 132 batches of cocrystal instances were gathered. After three batches were excluded, our proposed models were tested with these previously unseen molecules both before and after implementation of a batchwise retraining method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".