Author

Pablo Arbeláez

Universidad de Los AndesORCID 0000-0001-5244-2407Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Organism:yeast· 1

Frequent Co-Authors

Carolina Muñoz-CamargoiD· 2 shared Adriana Marcela Celis RamíreziD· 1 shared Chad LeidyiD· 1 shared Cristina GonzáleziD· 1 shared Juan C. CruziD· 1 shared Laura Alexandra DazaiD· 1 shared Marcela Manrique-MorenoiD· 1 shared Natalia Valderrama· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioengineering

Carolina Muñoz-CamargoORCID

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2 papers

Recent·Most cited

BioengineeringbioRxivPreprintAug 26, 2026

Assessing the translation of AI-prioritized genome-derived peptide fragments into validated antimicrobial candidates

Sebastian Ojeda, Paula Avila, Stiven Castellanos…·Carolina Muñoz-Camargo

This study evaluates AI-prioritized antimicrobial peptides derived from Malassezia furfur genomes, showing their antimicrobial activity and highlighting AI prediction biases.

DOI 10.64898/2026.08.25.747168

yeastproteomics

𝕏 PostBluesky

BioinformaticsPLoS ONEPeer-reviewedApr 26, 2021

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks.

Paola Ruiz Puentes, Natalia Valderrama, Cristina González…·Pablo Arbeláez

PharmaNet uses deep RNNs to predict drug-target interactions, achieving high accuracy in ligand prediction and identifying potential antiviral compounds.

21 citations

deep learningmachine learning (classical)

𝕏 PostBluesky