{"id":661,"date":"2026-06-03T05:52:23","date_gmt":"2026-06-03T05:52:23","guid":{"rendered":"https:\/\/idm-instituto.webs.upv.es\/wordpress\/?page_id=661"},"modified":"2026-07-30T21:52:11","modified_gmt":"2026-07-30T21:52:11","slug":"gamm-cybar","status":"publish","type":"page","link":"https:\/\/idm-instituto.webs.upv.es\/wordpress\/index.php\/gamm-cybar\/","title":{"rendered":"GAMM-CyBAR"},"content":{"rendered":"\n
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Group on Multivariate and Multicomponent Analysis – Quality and High – throughout Bioanalysis – GAMM-CyBAR<\/strong><\/p>\n<\/div>\n\n\n\n

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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. <\/p>\n\n\n\n

Currently, our investigations are focused around the development of artificial intelligence-based tools for the modelling of the enantioresolution in liquid chiral chromatography. <\/p>\n<\/div>\n<\/div>\n\n\n\n

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CV Prof. Salvador Sagrado Vives<\/strong><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

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.<\/p>\n<\/div>\n<\/div>\n\n\n\n

Research\u202fLines<\/strong>\u202f <\/p>\n\n\n\n

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Molecular and computational biointeraction. <\/p>\n<\/div>\n\n\n\n

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Chiral HPLC <\/p>\n<\/div>\n\n\n\n

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AI-Assisted models in Chemistry<\/p>\n<\/div>\n\n\n\n

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Latest<\/strong> Funding<\/strong><\/p>\n\n\n\n

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Desarrollo de nuevas estrategias basadas en la inteligencia artificial para la modelizaci\u00f3n inteligente en cromatograf\u00eda l\u00edquida quiral (DNEBIAMICLQ). PID2023-152340NB-I00<\/strong>. Agencia Estatal de Investigaci\u00f3n (AEI). 2024-2028. Mar\u00eda Jos\u00e9 Medina Hern\u00e1ndez.<\/em><\/p>\n\n\n\n

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Latest Publications<\/strong><\/p>\n\n\n\n

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Pardo-Cortina, C.; Escuder-Gilabert, L.; Sagrado, S. et al<\/em>. 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<\/em>. 2026<\/strong>. 19, 467038. 10.1016\/j.chroma.2026.467038<\/a> <\/p>\n\n\n\n

Pardo-Cortina, C.; Escuder-Gilabert L.; Medina-Hern\u00e1ndez, M.J. et al<\/em>. Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation\u2013Mobile Phase (EES\u2013MP) Profiles for MP Selection\u2500A Lux Cellulose-1 Case Study. Analytical Chemistry<\/em>. 2025<\/strong>. 98,1, 927-933. https:\/\/doi.org\/10.1021\/acs.analchem.5c06117<\/a> <\/p>\n\n\n\n

Gumede, N.J.; Bisetty, K.; Escuder-Gilabert L. et al<\/em>. 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<\/em>. 2025<\/strong>. 16, 102355. https:\/\/doi.org\/10.1016\/j.rechem.2025.102355<\/a><\/p>\n\n\n\n

Mart\u00edn-Biosca, Y.; Pardo-Cortina, C.; Escuder-Gilabert, L. et al<\/em>. Perspectivas de profesorado y alumnado frente a la inteligencia artificial en educaci\u00f3n superior. Actualidad Anal\u00edtica<\/em>. 2025<\/strong>. 89, 4-11. https:\/\/doi.org\/10.69856\/AA.2025.541124<\/a><\/p>\n\n\n\n

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.<\/em> 2024<\/strong>. 942, 173754. https:\/\/doi.org\/10.1016\/j.scitotenv.2024.173754<\/a><\/p>\n\n\n\n

Sagrado S.; Pardo-Cortina, C.; Escuder-Gilabert L. et al<\/em>. Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems? Analytical Chemistry<\/em>. 2024<\/strong>. 96, 29, 12205-12212. https:\/\/doi.org\/10.1021\/acs.analchem.4c02656<\/a><\/p>\n\n\n\n

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Carlos Pardo Cortina <\/p>\n<\/div>\n<\/div>\n\n\n\n

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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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-661","page","type-page","status-publish","hentry"],"yoast_head":"\nGAMM-CyBAR – Instituto Interuniversitario de Reconocimiento Molecular y Desarollo Tecnol\u00f3gico<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/idm-instituto.webs.upv.es\/wordpress\/index.php\/gamm-cybar\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GAMM-CyBAR – Instituto Interuniversitario de Reconocimiento Molecular y Desarollo Tecnol\u00f3gico\" \/>\n<meta property=\"og:description\" content=\"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. 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