# Molecular Property Prediction

SMI-SSED is a Mamba-based chemical foundation model that supports various complex tasks such as quantum property prediction, achieving state-of-the-art performance on multiple benchmark datasets.

TwinBooster is a DeBERTa V3 base model fine-tuned on the PubChem bioassay corpus, combined with the Barlow Twins self-supervised learning method for molecular property prediction.

A neural language model toolkit for pretraining and fine-tuning SMILES-based molecular language models, supporting semi-supervised learning

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.