dorsal/arxiv
View SchemaTransformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds
| Authors | Bo Pan, Zhiping Zhang, Kevin Spiekermann, Tianchi Chen, Xiang Yu, Liying Zhang, Liang Zhao |
|---|---|
| Categories | |
| ArXiv ID | 2601.07930vv1 |
| URL | https://arxiv.org/abs/2601.07930 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
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.
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"abstract": "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\u0027s ability to generate chemically valid transformations, explore diverse chemical spaces, and maintain scalability across varying search sizes.",
"arxiv_id": "2601.07930",
"authors": [
"Bo Pan",
"Zhiping Zhang",
"Kevin Spiekermann",
"Tianchi Chen",
"Xiang Yu",
"Liying Zhang",
"Liang Zhao"
],
"categories": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds",
"url": "https://arxiv.org/abs/2601.07930",
"version": "v1"
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