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.

SmileBERTa 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.

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

COMO (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.

Drugs must satisfy stringent criteria for both efficacy and safety. This model predicts the likelihood of FDA approval for small-molecule drugs, represented using SMILES (Simplified Molecular Input Line Entry System) strings.

This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset derived ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded…