This OpenAD service provides access to SMILES-based Transformer Encoder-Decoder (SMI-TED), a foundation model for materials science and chemistry. SMI-TED is an encoder-decoder model pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED offers several predictive models, including quantum property prediction, with two main variants ( 289 M and 8 × 289 M ).
More information:
github.com/IBM/materials
huggingface.co/ibm/materials.smi-ted
arxiv.org/abs/2407.20267
For instructions on how to deploy and use this service in OpenAD, please refer to the OpenAD docs.
Note
The current public version of app.py includes only QM9 properties from MoleculeNet. You can augment this to include QM8 and additional MoleculeNet properties (grouped under MoleculeNet) by changing the selected algorithms list, if the model checkpoints are available.