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README.md

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+---
+tags:
+- chemistry
+- smiles
+- molecules
+- cheminformatics
+- classification
+pretty_name: Cocrystal Classification
+size_categories:
+- 1K<n<10K
+---
+
+# Cocrystal Classification
+
+Binary classification dataset for predicting cocrystal formation given two small molecules represented as SMILES.
+
+For single‑sequence encoder models (e.g., BERT‑style), we provide `smiles_concat`, which concatenates `smiles_a` and `smiles_b` with a period separator: `smiles_a.smiles_b`. For Bi-encoder and Cross‑encoder models, use the provided `smiles_a` and `smiles_b` fields directly.
+
+## Source
+- Original dataset page: https://sites.google.com/view/medardemswahili/publications-awards#h.kcrgyq3r642s
+
+## Data fields
+- `smiles_a` (string): Active Pharmaceutical Ingredient (API) SMILES. Unmodified from the source.
+- `smiles_b` (string): Coformer SMILES. Unmodified from the source.
+- `smiles_concat` (string): Concatenation of `smiles_a` and `smiles_b` with a period, for encoder models.
+- `label` (int): Binary class, {0: None, 1: Cocrystal}.
+
+## Citation
+Mswahili, M.E.; Lee, M.-J.; Martin, G.L.; Kim, J.; Kim, P.; Choi, G.J.; Jeong, Y.-S. Cocrystal Prediction Using Machine Learning Models and Descriptors. Applied Sciences, 2021, 11, 1323. https://doi.org/10.3390/app11031323
+
+Please cite the authors above if you use this dataset.