Author Archives: Vera

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Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows

Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows

December 10, 2025

Ramon Adàlia, Paula Cifuentes, Joyce Liu, Lionel Cheruzel, Gemma Sanjuan, Tomàs Margalef, Ismael Zamora

Abstract

Metabolite identification is a pivotal step in drug discovery and development, enabling the comprehensive analysis of drug-derived compounds within biological systems. However, the complexity of liquid chromatography–mass spectrometry data often results in numerous false positives, complicating the identification of true metabolites. This study introduces a machine-learning-based approach to improve the accuracy of false positive detection in metabolite identification workflows. By incorporating expert knowledge, we develop a feature set for metabolite-related chromatographic peaks that characterizes true and false positives with high accuracy, integrating data from mass spectra, chromatographic signals, and kinetic profiles. We validate this method via gradient boosting decision tree classifiers on both publicly available and proprietary “real-world” data sets, including small molecules and new modalities. Our findings demonstrate that machine learning-assisted techniques significantly reduce false positive identifications, thereby increasing the efficiency and accuracy of metabolite identification processes.

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Prediction of peptide cleavage sites using protein language models and graph neural networks

Prediction of peptide cleavage sites using protein language models and graph neural networks

October 30, 2025

Paula Cifuentes, Ramon Adàlia, Ismael Zamora

Abstract

The growing interest in using peptide molecules as therapeutic agents, driven by their high selectivity and efficacy, has become a significant trend in the pharmaceutical industry. However, their oral administration remains challenging due to their low bioavailability and vulnerability to proteases, which produce the cleavage of peptide bonds. To optimize peptide drug development, in silico tools based on machine learning algorithms have been developed for site of cleavage prediction. These tools, which rely on manual feature extraction, have limitations in capturing complex peptide structures, especially those involving non-natural amino acids or cyclic peptides. This study presents two novel in silico approaches for cleavage site prediction. The first approach uses protein language models, specifically ESM-2, which has been fine- tuned to leverage its learned peptide structure embeddings for accurate cleavage site prediction, eliminating the need for manual feature engineering. The second approach employs graph neural networks, representing peptides via hierarchical graphs at the atom and amino acid levels, effectively handling cyclic peptide structures, including those containing non-natural amino acids. The applicability of this second approach is shown through a case study on a set of four cyclic peptides containing non-natural amino acids, comparing in silico predictions with experimental data.

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