# Drug Discovery

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 Transformer-based encoder-decoder model specifically designed for molecular representation using SELFIES

MoLFormer is a chemical language model pre-trained on 1.1 billion molecular SMILES strings from ZINC and PubChem. This version uses 10% samples from each dataset for training.

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.

This model is a cheminformatics model pretrained on 10 million SMILES strings from the PubChem database, primarily used for molecular representation learning and chemical property prediction.