# Molecular Generation

GP-MoLFormer is a chemical language model pretrained on 650 million to 1.1 billion molecular SMILES string representations from ZINC and PubChem, focusing on molecular generation tasks.

A large-scale molecular generation model based on the SELFIES molecular language, capable of de novo molecular generation or completing partial molecular structures.

This model uses WGAN-GP and R-GCN architectures to generate novel molecular graph structures, accelerating the drug discovery process.

ChemGPT is a generative molecular modeling tool based on the GPT-Neo model, specializing in molecular generation and research in the field of chemistry.

ChemGPT is a generative molecular modeling Transformer model based on the GPT-Neo architecture, pretrained on the PubChem10M dataset.