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This model is part of the **PeptideMTR** suite, to be released in a forthcoming paper. It is designed for multi-target biochemical property prediction. - **Architecture:** BERT-based Transformer - **Pretraining:** MLM - **Input:** SMILES string All models in this suite are designed for simplicity. For inference, the model accepts a SMILES string directly.

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This model is part of the **PeptideMTR** suite, to be released in a forthcoming paper. It is designed for multi-target biochemical property prediction. - **Architecture:** BERT-based Transformer - **Pretraining:** MLM - **Input:** SMILES string All models in this suite are designed for simplicity. For inference, the model accepts a SMILES string directly.

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This model is part of the **PeptideMTR** suite, to be released in a forthcoming paper. It is designed for multi-target biochemical property prediction. - **Architecture:** BERT-based Transformer - **Pretraining:** MTR-only - **Input:** SMILES string All models in this suite are designed for simplicity. For inference, the model accepts a SMILES string directly.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Peptide-trained Chemical Language Model using 10.8M peptides and 12.6M small molecules for MLM pretraining. Loading the tokenizer is not possible with transformers. A custom tokenizer must be loaded from the 'tokenizer' directory found at at https://github.com/AaronFeller/PeptideCLM An example script for this can be found in the repository. A short example is below (note, the tokenizer directory must be downloaded):

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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