akhljndl/smollm
https://huggingface.co/akhljndl/smollmA 53K-parameter weight-shared transformer that learns SMILES grammar by applying one small block 8 times. It reaches 95.3% validity on ZINC-250K — outperforming an unshared GPT 10× larger (87.6%).
Sourced from
- HuggingFace — akhljndl/smollm
Related resources
sagawa/ReactionT5v1-forward
by sagawaThis is a ReactionT5 pre-trained to predict the products of reactions.
Keylab/COMO
by KeylabCOMO (Closed-loop Optical Molecule recOgnition) is a deep learning framework for Optical Chemical Structure Recognition (OCSR). It recognizes chemical structure diagrams from images and predicts SMILES strings with atom-level 2D coordinates and bond matrices.
sagawa/ReactionT5v2-forward
by sagawaThis is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.
himasai9712/smiles2odor-pom-gnn
by himasai9712Predict olfactory descriptors from SMILES strings. A GINE-based graph neural network trained on the Leffingwell PMP dataset, reproducing the Principal Odor Map (POM) approach.
Hari5115/molecular-odor-predictor
by Hari5115A PyTorch MLP that predicts odor descriptors from a molecule's SMILES string using Morgan (ECFP4) fingerprints. Given any molecule, the model outputs a smell profile across 50 odor categories.
NovoMolGen is a family of molecular foundation models trained on 1.5 billion ZINC-22 molecules with Llama architectures and FlashAttention. It achieves state-of-the-art performance on both unconstrained and goal-directed molecule generation tasks.