Group on Multivariate and Multicomponent Analysis – Quality and High – throughout Bioanalysis – GAMM-CyBAR
The Group on Multivariate and Multicomponent analysis – Quality and High-throughout Bioanalysis (GAMM-CyBAR), a multidisciplinary research team made up of 4 senior researchers and multiple postgraduate and undergraduate students. GAMM-CyBAR is recognised for its experience in the development of methodologies for the separation of enantiomers from chiral compounds, and for the development and evaluation of chromatographic and electrophoretic systems to study the pharmacological, toxicological and eco toxicological behaviour of xenobiotics. The group also stands out in fields such as modelling of chromatographic parameters, characterisation of molecular interactions, in silico computational simulation through Molecular Docking, and the evaluation of eco toxicological parameters.
Currently, our investigations are focused around the development of artificial intelligence-based tools for the modelling of the enantioresolution in liquid chiral chromatography.
CV Prof. Salvador Sagrado Vives
Salvador is PhD in Chemistry from the University of Valencia (1988) and Full Professor since 2009, he has built a distinguished career in analytical and pharmaceutical chemistry. His research focuses on advanced analytical techniques (chromatography, capillary electrophoresis), chemometrics, and predictive modeling, with recent incorporation of artificial intelligence applied to chemical data.
He has extensive experience in national and international research projects, industry collaborations, and patented technologies, and has authored over 150 high-impact international publications. He has also supervised numerous theses at all academic levels.
Research Lines
Molecular and computational biointeraction.
Chiral HPLC
AI-Assisted models in Chemistry
Latest Funding
Desarrollo de nuevas estrategias basadas en la inteligencia artificial para la modelización inteligente en cromatografía líquida quiral (DNEBIAMICLQ). PID2023-152340NB-I00. Agencia Estatal de Investigación (AEI). 2024-2028. María José Medina Hernández.
Latest Publications
Pardo-Cortina, C.; Escuder-Gilabert, L.; Sagrado, S. et al. Impact of selector regioisomerism on mixed-mode retention and enantioresolution: Comparative evaluation of 3-chloro-4-methyl and 4-chloro-3-methyl substituted cellulose chiral stationary phases. Journal of Chromatography A. 2026. 19, 467038. 10.1016/j.chroma.2026.467038
Pardo-Cortina, C.; Escuder-Gilabert L.; Medina-Hernández, M.J. et al. Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation–Mobile Phase (EES–MP) Profiles for MP Selection─A Lux Cellulose-1 Case Study. Analytical Chemistry. 2025. 98,1, 927-933. https://doi.org/10.1021/acs.analchem.5c06117
Gumede, N.J.; Bisetty, K.; Escuder-Gilabert L. et al. Prospective evaluation of CYP19A1-mediated aromatase inhibitors for ER+ breast cancers by ultra-high performance liquid chromatography tandem mass spectrometry supported by in silico methods. Results in Chemistry. 2025. 16, 102355. https://doi.org/10.1016/j.rechem.2025.102355
Martín-Biosca, Y.; Pardo-Cortina, C.; Escuder-Gilabert, L. et al. Perspectivas de profesorado y alumnado frente a la inteligencia artificial en educación superior. Actualidad Analítica. 2025. 89, 4-11. https://doi.org/10.69856/AA.2025.541124
Lephalala, M.; Sagrado S.; Bisetty, K. Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants. Science of the Total Environment. 2024. 942, 173754. https://doi.org/10.1016/j.scitotenv.2024.173754
Sagrado S.; Pardo-Cortina, C.; Escuder-Gilabert L. et al. Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems? Analytical Chemistry. 2024. 96, 29, 12205-12212. https://doi.org/10.1021/acs.analchem.4c02656
Carlos Pardo Cortina