2025/03/19 by João Capela, João Cheixo, Dick de Ridder +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · Engineering · Medicine · #Abiotic component #Artificial intelligence #Biochemical engineering #Biology #Computational biology #Computer science #Ecology #Engineering #Machine learning #Microbial Natural Products and Biosynthesis #Pipeline (software) #Plant Gene Expression Analysis #Plant biochemistry and biosynthesis #Programming language #Task (project management)
paper · pdf · doi:10.1515/jib-2024-0050
openalex publication_date 2025/03/19 · openalex created_date 2025/03/20 · openalex updated_date 2026/08/06
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. The pipeline and models are available at the repository: https://github.com/jcapels/SMPrecursorPredictor.