J Med CBR Def | Volume 7, 2009 Submitted 22 February 2009 | Accepted 3 June 2009 | Revised 22 June 2009 | Published 4 August 2009 1 Utilizing The Quantum Intelligence System For Drug Discovery (QIS D2) For Anti-HIV And Anti- Cancer Cocktail Detection YING ZHAO, SHERRY WEI, IAN OGLESBY AND CHARLES ZHOU Quantum Intelligence, Inc. 3375 Scott Blvd, Suite 100 Santa Clara, Ca 95054 Suggested citation: Zhao, Y.; Wei, S.; Oglesby, I.; Zhou, C. (2009), “Utilizing The Quantum Intelligence System For Drug Discovery (QIS D 2 ) For Anti-HIV And Anti-Cancer Cocktail Detection”, JMedCBR 7, 7 July 2009, http://www.jmedcbr.org/issue0701/Zhou/Zhou_07_09.html. ABSTRACT Screening for a chemical’s affinity with biological targets and/or molecular targets (e.g., proteins and genes) is critical in a typical drug development process. The most common method for drug-target affinity screening is to computationally “dock” small molecules or ligands into a molecular target. For example, the interactions between the countermeasure human acetylcholinesterase (HuAChE) and the nerve agent VX have been studied using a method similar to docking. However, this docking method has its shortcomings, complicated by insufficient 3D structure data for certain protein targets and the existence of different drug-protein binding mechanisms outside of the prototypical “lock and key” model. The Quantum Intelligence System for Drug Discovery, QIS D 2 , is a screening system designed to predict and extrapolate the affinity between a chemical and its molecular target using samples of experimental data. Previously, we showed that the QIS D 2 system is capable of handling ~40,000 chemicals, performing automatic sequence clustering using about ~1200 structure fragments, and accurately predicting the chemicals’ impacts on ~60 efficacy targets, ~500 toxicity targets, ~11,000 gene targets, ~200 molecular targets, and ~60 pathway targets. Here, we extended the application of the QIS D 2 system to examine its capabilities in screening two FDA approved drug cocktails, anti- HIV and anti-cancer, and compared our results to publicly available data. Our findings indicate