Keylab/COMO
https://huggingface.co/Keylab/COMOCOMO (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.
Sourced from
- HuggingFace — Keylab/COMO
Related resources
akhljndl/smollm
by akhljndlA 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%).
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.
NisargRhino/SmileBERTa
by NisargRhinoSmileBERTa is a RoBERTa-based language model for chemistry, built on the ChemBERTa architecture and fine-tuned to predict small-molecule fragment SMILES from full small-molecule drug SMILES.
little1d/C-H-Formula
by little1dThis repository contains NMRTrans checkpoints for C-NMR + H-NMR + Formula input.
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.
sagawa/ReactionT5v1-forward
by sagawaThis is a ReactionT5 pre-trained to predict the products of reactions.