2025/09/17 by Sofia Larsson, Miranda Carlsson, Richard Beckmann +2 · 1 voice
Computer Science · Biochemistry, Genetics and Molecular Biology · Chemistry · #Computational Drug Discovery Methods #Metabolomics and Mass Spectrometry Studies #Analytical Chemistry and Chromatography
paper · doi:10.1016/j.ailsci.2025.100142
Metabolite identification studies are an essential but costly and time-consuming component of drug development. Computational methods have the potential to accelerate early-stage drug discovery, particularly with recent advances in deep learning which offer new opportunities to accelerate the process of metabolite prediction. We present LAGOM (Language-model Assisted Generation Of Metabolites), a Transformer-based approach built upon the Chemformer architecture, designed to predict likely metabolic transformations of drug candidates. Our results show that LAGOM performs competitively with, and in some cases surpasses, existing state-of-the-art metabolite prediction tools, demonstrating the potential of language-model-based architectures in chemoinformatics. By integrating diverse data sources and employing data augmentation strategies, we further improve the model’s generalisation and predictive accuracy. The implementation of LAGOM is publicly available at github.com/tsofiac/LAGOM .