Work

Predicting precursors of plant specialized metabolites using DeepMol automated machine learning

Year: 2025

Type: article

Abstract: 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.

Source: Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics

DOI: 10.1515/jib-2024-0050

Authors: João Capela, João Cheixo, Dick de Ridder, Oscar Dias, Miguel Rocha

Institutions: University of Minho, Wageningen University & Research, Labbels

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,187

Funder: Fundação para a Ciência e a Tecnologia