Update README.md

README.md

@@ -4,6 +4,11 @@ PoCo is a feature extractor for polymer structures.
 
 It takes polymer SMILES strings as input and returns 512-dimensional vectors, which can be used as polymer representations for downstream tasks such as property prediction.
 
+## Resources
+
+- Paper: [Contrastive representation learning for polymer informatics](https://doi.org/10.26434/chemrxiv.15003645/v1)
+- Code: [GitHub repository](https://github.com/crema-lida/PoCo)
+
 ## Prerequisites
 
 Install either `sentence-transformers` (recommended), or
@@ -129,13 +134,20 @@ apply the mean pooling step shown in this section.
 
 ## Input Notes
 
-- Polymer SMILES must use `[*]` to mark repeat-unit endpoints, not bare `*`.
-- The model does not validate whether a string is a chemically valid SMILES
+- Polymer SMILES **must** use `[*]` to mark repeat-unit endpoints, not bare `*`.
+- The model does **not** validate whether a string is a chemically valid SMILES
   string. We recommend canonicalizing polymer SMILES with the [`psmiles`](https://psmiles.readthedocs.io/) library before passing them to the model.
 
 ## Citation
 
 If you use PoCo, please cite:
 
-Wang, L.; Long, D. *Contrastive representation learning for polymer
-informatics*. ChemRxiv, 2026. https://doi.org/10.26434/chemrxiv.15003645/v1
+```bibtex
+@article{wang2026poco,
+  title = {Contrastive representation learning for polymer informatics},
+  author = {Wang, Lida and Long, Donghui},
+  journal = {ChemRxiv},
+  year = {2026},
+  doi = {10.26434/chemrxiv.15003645.v1}
+}
+```