--- license: mit pipeline_tag: feature-extraction tags: - chemistry - multimodal --- # GLACIER: Graph-Language Alignment for Chemical Inference and Exploration using Representations GLACIER is a multimodal student-teacher foundation model designed for molecular property prediction. It integrates molecular graphs, SMILES strings, and physicochemical descriptors to learn rich molecular embeddings. - **Paper:** [GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction](https://huggingface.co/papers/2606.11382) - **Repository:** [https://github.com/eemokey/glacier](https://github.com/eemokey/glacier) ## Sample Usage Since this model uses a custom architecture, you need to download the repository files to load the model. ```python import torch from huggingface_hub import snapshot_download import sys # Download the repository to access custom model code repo_dir = snapshot_download(repo_id="glacier-hf/GLACIER-100k-MiniMol") sys.path.append(repo_dir) from data.dataloader import SmilesMoleculeDataset, build_dataloader from glacier_student import Glacier # Load the pretrained GLACIER model model = Glacier.from_pretrained("glacier-hf/GLACIER-100k-MiniMol") # Prepare input data dataset = SmilesMoleculeDataset(smiles=["Cn1c(=O)c2c(ncn2C)n(C)c1=O"]) dataloader = build_dataloader(dataset, batch_size=1) model.eval() batch = next(iter(dataloader)) with torch.no_grad(): embedding = model(batch) print(embedding) ``` ## GLACIER Model Files - `dataloader`: customized dataloader for multimodal learning - `encoders`: graph, text, and tabular encoders - `fusion`: Finsler geometry-aware fusion method - `glacier_student`: GLACIER model backbone and contrastive loss - `utils`: miscellaneous helper functions ## Citation ```bibtex @inproceedings{nguyen2026glacier, title={GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction}, author={Emily Nguyen and Yongchan Hong and Harsh Toshniwal and Yan Liu and Andreas Luttens}, booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD ’26)}, year={2026}, publisher={ACM}, doi={10.1145/3770855.3819032} } ```
