datasetter458/bulk-chem-v1.0

Predict olfactory descriptors from SMILES strings. A GINE-based graph neural network trained on the Leffingwell PMP dataset, reproducing the Principal Odor Map (POM) approach.

This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.

State-of-the-Art Geometric GNN for Predicting Molecular Properties

Molexar-10M Base is the unconditional base model for Molexar, a unified multimodal molecular foundation model for drug design. It is trained as an autoregressive molecular language model over Fragment-SELFIES, a BRICS-fragment molecular language with validity-preserving decoding and…

Gemma 4 E2B fine-tuned on 225K drug–target pairs for novel small-molecule generation.

NucleusDiff