Publicação
Predicting precursors of plant specialized metabolites using DeepMol automated machine learning
| Resumo: | Plants produce specialized metabolites, which play critical roles in defending against biotic and abiotic stresses. Due to their chemical diversity and bioactivity, these compounds have significant economic implications, particularly in the pharmaceutical and agrotechnology sectors. Despite their importance, the biosynthetic pathways of these metabolites remain largely unresolved. Automating the prediction of their precursors, derived from primary metabolism, is essential for accelerating pathway discovery. Using DeepMol's automated machine learning engine, we found that regularized linear classifiers offer optimal, accurate, and interpretable models for this task, outperforming state-of-the-art models while providing chemical insights into their predictions. |
|---|---|
| Autores principais: | Capela, João |
| Outros Autores: | Cheixo, João; de Ridder, Dick; Dias, Oscar; Rocha, Miguel |
| Assunto: | biosynthesis machine learning plant specialized metabolites |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Plants produce specialized metabolites, which play critical roles in defending against biotic and abiotic stresses. Due to their chemical diversity and bioactivity, these compounds have significant economic implications, particularly in the pharmaceutical and agrotechnology sectors. Despite their importance, the biosynthetic pathways of these metabolites remain largely unresolved. Automating the prediction of their precursors, derived from primary metabolism, is essential for accelerating pathway discovery. Using DeepMol's automated machine learning engine, we found that regularized linear classifiers offer optimal, accurate, and interpretable models for this task, outperforming state-of-the-art models while providing chemical insights into their predictions. |
|---|
Atividades financiadas
Carregando projetos financiados...