Albert Katchborian Neto

Bachelor in Pharmacy (2018) from the Federal University of Alfenas (UNIFAL/MG), Master in Organic Chemistry (2020), and PhD in Organic Chemistry (2024) from the same institution, with a focus on the subfields of Natural Products Chemistry and Metabolomics. During his undergraduate studies (20122014), he was an active member of the Academic League of Pharmacotherapeutics (LAFarT - UNIFAL) and participated in a sandwich exchange program at the University of Adelaide, Australia, in the Health Sciences Institute (2015). As part of his undergraduate internship (2018), he conducted research at the Laboratory of Phytochemistry, Medicinal Chemistry, and Metabolomics (LFQMM - UNIFAL), gaining experience in advanced separation and characterization techniques, such as High-Performance Liquid Chromatography (HPLC), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS). During his Masters program, he participated in an academic exchange program at the Abel Salazar Biomedical Sciences Institute of the University of Porto, Portugal, focusing on marine natural products chemistry. Subsequently, he undertook a sandwich PhD program at the Institute of Biomedical Sciences at the University of Strathclyde, Scotland, UK (2024). Currently, he is a postdoctoral researcher at the Federal University of ABC (UFABC), at the Center for Natural and Humanities Sciences. Additionally, he is an active member of the Academic League of Natural and Psychedelic Products (LAPNP - UNIFAL/MG) and a collaborator at the Neuroimaging and Psychophysiology Laboratory at UFRJ-RJ.He is dedicated to the bioprospecting of natural products, with a current focus on compounds with psychedelic and anti-inflammatory activity. His research integrates advanced metabolomic strategies for the identification of biomarkers in complex natural matrices, aiming to develop innovative therapeutic applications. His work spans from the chemical characterization of extracts to the study of biological samples, seeking to understand and predict metabolic changes associated with different biological states. He has solid experience in computational metabolomic and chemometric tools, combining machine learning with high-resolution MS and NMR data. These approaches enable the discovery of new bioactive substances, the characterization of natural matrices with therapeutic potential, and the development of predictive models for metabolic responses. (Source: Lattes Curriculum)