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README.md
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For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.
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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 $8 \times 289M$). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.
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For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.
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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 $8 \times 289M$). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.
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