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# SMILES-based Transformer Encoder-Decoder (SMI-TED)
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This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
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Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/smi_ted_preprint.pdf)
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For more information contact: [email protected] or [email protected].
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## Introduction
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We present a large encoder-decoder chemical foundation model, SMILES-based Transformer Encoder-Decoder (SMI-TED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants (289M and 8X289M). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: [email protected] or [email protected].
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# SMILES-based Transformer Encoder-Decoder (SMI-TED)
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This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
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Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/smi_ted_preprint.pdf)
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For more information contact: [email protected] or [email protected].
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## Introduction
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We present a large encoder-decoder chemical foundation model, SMILES-based Transformer Encoder-Decoder (SMI-TED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants (289M and 8X289M). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: [email protected] or [email protected].
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