Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914250688036864 |
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| author | Pan, Bo Zhang, Zhiping Spiekermann, Kevin Chen, Tianchi Yu, Xiang Zhang, Liying Zhao, Liang |
| author_facet | Pan, Bo Zhang, Zhiping Spiekermann, Kevin Chen, Tianchi Yu, Xiang Zhang, Liying Zhao, Liang |
| contents | Functional group replacement is a pivotal approach in cheminformatics to enable the design of novel chemical compounds with tailored properties. Traditional methods for functional group removal and replacement often rely on rule-based heuristics, which can be limited in their ability to generate diverse and novel chemical structures. Recently, transformer-based models have shown promise in improving the accuracy and efficiency of molecular transformations, but existing approaches typically focus on single-step modeling, lacking the guarantee of structural similarity. In this work, we seek to advance the state of the art by developing a novel two-stage transformer model for functional group removal and replacement. Unlike one-shot approaches that generate entire molecules in a single pass, our method generates the functional group to be removed and appended sequentially, ensuring strict substructure-level modifications. Using a matched molecular pairs (MMPs) dataset derived from ChEMBL, we trained an encoder-decoder transformer model with SMIRKS-based representations to capture transformation rules effectively. Extensive evaluations demonstrate our method's ability to generate chemically valid transformations, explore diverse chemical spaces, and maintain scalability across varying search sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07930 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds Pan, Bo Zhang, Zhiping Spiekermann, Kevin Chen, Tianchi Yu, Xiang Zhang, Liying Zhao, Liang Machine Learning Functional group replacement is a pivotal approach in cheminformatics to enable the design of novel chemical compounds with tailored properties. Traditional methods for functional group removal and replacement often rely on rule-based heuristics, which can be limited in their ability to generate diverse and novel chemical structures. Recently, transformer-based models have shown promise in improving the accuracy and efficiency of molecular transformations, but existing approaches typically focus on single-step modeling, lacking the guarantee of structural similarity. In this work, we seek to advance the state of the art by developing a novel two-stage transformer model for functional group removal and replacement. Unlike one-shot approaches that generate entire molecules in a single pass, our method generates the functional group to be removed and appended sequentially, ensuring strict substructure-level modifications. Using a matched molecular pairs (MMPs) dataset derived from ChEMBL, we trained an encoder-decoder transformer model with SMIRKS-based representations to capture transformation rules effectively. Extensive evaluations demonstrate our method's ability to generate chemically valid transformations, explore diverse chemical spaces, and maintain scalability across varying search sizes. |
| title | Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.07930 |