Jose L. Medina-Franco
Torrey Pines Institute for Molecular Studies
Dr. Jose Luis Medina-Franco received a Bachelor of Science in chemistry from the National Autonomous University of Mexico (UNAM) in 1998 and that same year, he joined Procter & Gamble in Mexico City, working in the research and development department. He received a Masters in Science in 2002 ("Synthesis of pyridinone-2(1H)-ones as precursors of potential HIV-1 Reverse Transcriptase Inhibitors") and a Ph.D. degree in 2005 ("Computer-Aided Drug Design. HIV-1 Reverse Transcriptase Inhibitors, Hypocholesterolemic and Antiparasitic Compounds"), both in Chemisty from UNAM, working in the laboratory of Professor Rafael Castillo. During his doctoral studies, Dr. Medina-Franco participated in research visits with Professor Jesus Jimenez-Barbero, in Madrid, Spain; Professor Alexander Tropsha at the University of North Carolina at Chapel Hill and Professor Gerald M. Maggiora at the University of Arizona. In 2005, he joined Professor Maggiora as a postdoctoral fellow. In August 2007, Dr. Medina-Franco was named Assistant Member at the Torrey Pines Institute for Molecular Studies in Florida.
Dr. Jose Medina-Franco is using computational approaches to boost the discovery of chemical compounds that have selected molecular interactions with anti-cancer targets in the search for new drug therapies. His group conducts computational screening of large compound databases to identify novel compounds directed to targets associated with the treatment of cancer, HIV/AIDS and other diseases. His laboratory also uses molecular modeling to help uncover the mechanism of action of biologically active compounds and guide the development of drug candidates. In close collaboration with chemists and biologists at TPIMS and research groups abroad, Dr. Medina-Franco’s group has identified novel and potent inhibitors of the enzymes DNA methyltransferase and protein kinase B. Dr. Medina-Franco’s research group also uses and develops chemoinformatic methods to study structure-activity relationships of compound data sets for drug discovery.