Leveraging large language models for enzymatic reaction prediction and characterization

We present a systematic study of multitask Large Language Models, fine-tuned to predict enzyme commission numbers and to perform forward and retrosynthesis of enzymatic reactions.

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Publication Details

Journal
Digital Discovery
Published
2025-01-01
DOI
https://doi.org/10.1039/d5dd00187k
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Leveraging large language models for enzymatic reaction prediction and characterization

Jana M. Weber, Lorenzo Di Fruscia
Digital Discovery
Machine Learning in Materials Science
article

Leveraging large language models for enzymatic reaction prediction and characterization

Jana M. Weber, Lorenzo Di Fruscia
article en

Abstract

We present a systematic study of multitask Large Language Models, fine-tuned to predict enzyme commission numbers and to perform forward and retrosynthesis of enzymatic reactions.

Digital DiscoveryVol. 4(12)
Delft University of Technology (NL)
Rijksdienst voor Ondernemend Nederland
Openalex Percentile: Top 75%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Leveraging large language models for enzymatic reaction prediction and characterization — Jana M. Weber, Lorenzo Di Fruscia · Digital Discovery (2025) | TGRS Research Map | TGRS