Billy W. Day is professor of pharmaceutical sciences, with secondary appointments in chemistry, environmental & occupational health and the clinical translational science institute, and a visiting professor appointment in computational biology, at the University of Pittsburgh. He is also the director of medicinal chemistry in the University of Pittsburgh Drug Discovery Institute. He received the B.S. in chemistry and biology from Oklahoma City University in 1982. He obtained the Ph.D. in pharmaceutical sciences (medicinal chemistry) from the University of Oklahoma in 1988, working on the design, synthesis and biological evaluation of antiestrogens. He did his postdoctoral training in chemistry and toxicology at the Massachusetts Institute of Technology, studying the formation, detection and quantitation of carcinogen metabolite-protein adducts. He joined the faculty at the University of Pittsburgh in 1991. Professor Day's research has centered on the chemistry and pharmacology/toxicology of cancer, particularly anti-tubulin/microtubule agents, antiestrogens, anti-topoisomerase II agents, and nitric oxide and pro/anti-oxidant biochemistry, although work in recent years has also included agents that alter the innate immune response, alter signaling pathways important for damaged organ repair/repopulation, inhibit HIV 1-host cell protein-protein interactions, and inhibitors/enhancers of the molecular motor protein complex cytoplasmic dynein. His lab designs and synthesizes potential drugs/chemical biology tools, then performs biochemical and cell biological evaluations of them (along with a large number of other chemicals provided by a variety of collaborators). The lab also performs radio- and stable isotope-labeled syntheses. The biological evaluations include a large mass spectrometry-based component, i.e., metabolite identification, as well as discovery and structure elucidation of biomarkers of effect or predisposition, and their quantitative analyses via stable isotope methodologies. All of the chemical and biological data is used for computational redesign and/or building of predictive tools, such as quantitative structure-activity relationships or pathway perturbation analyses.