sagawa/ReactionT5v2-retrosynthesis

This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.

## Example Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration

This is a ReactionT5 pre-trained to predict the products of reactions.

# DrugGen 2: A disease-aware language model for enhancing drug discovery DrugGen-2 is a disease‑aware language model specialized for generating drug-like SMILES structures based on both disease pathways and protein sequence.

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.

This is an official model checkpoint for Asclepius-Mistral-7B-v0.3 (arxiv). This model is an enhanced version of Asclepius-7B, by replacing the base model with Mistral-7B-v0.3 and increasing the max sequence length to 8192.