We will create a comprehensive computational drug discovery platform by enhancing a novel technique for a dynamic, fragment based, screening of small molecule compounds against the three dimensional structures of multiple protein targets from infectious disease causing pathogens, followed by prospective in vitro and in vivo experimental verification. We will further modify the most promising lead candidates computationally and screen them against all known human proteins and variants simultaneously to assess for side effects against essential proteins, and to ensure that they possess safe and effective absorption, distribution, metabolism, and excretion profiles against major proteins in known drug delivery pathways. The top ranking leads will again be experimentally verified, and the computational protocol will be itera- tively refined using machine learning techniques. We will initially focus on discovering preclinical drug candidates against infections caused by all eight human herpes viruses (HHVs). This virus family infects billions of humans worldwide every year and is the source of significant mortality in immunocompromised patients. Broad spectrum therapeutics against these key pathogens will benefit the entire global community. In contrast to other computational efforts, my group has successfuly applied and experimentally verified their predictions of inhibitors to treat herpes, malaria, and dengue. This was accomplished at a fraction of the time, effort, and cost typically required by pharmaceutical companies. Our significant successes thus far attest to the efficacy of our drug discovery technologies. The Pioneer Award funds will therefore allow us to bridge the gap of discovering computationally predicted lead compounds and demonstrating their preclinical effectiveness for further clinical and therapeutic use. The ultimate goal is to create a comprehensive computational drug discovery pipeline, applicable to any disease, thereby increasing the suc

National Institute of Health (NIH)
National Library of Medicine (NLM)
NIH Director’s Pioneer Award (NDPA) (DP1)
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Study Section
Special Emphasis Panel (ZGM1-NDPA-B (01))
Program Officer
Ye, Jane
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University of Washington
Schools of Medicine
United States
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Chopra, Gaurav; Kaushik, Sashank; Elkin, Peter L et al. (2016) Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules 21:
Craig, Justin K; Risler, Jenni K; Loesch, Kimberly A et al. (2016) Mycobacterium Cytidylate Kinase Appears to Be an Undruggable Target. J Biomol Screen 21:695-700
Chopra, Gaurav; Samudrala, Ram (2016) Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform. Curr Pharm Des 22:3109-23
Manocheewa, Siriphan; Mittler, John E; Samudrala, Ram et al. (2015) Composite Sequence-Structure Stability Models as Screening Tools for Identifying Vulnerable Targets for HIV Drug and Vaccine Development. Viruses 7:5718-35
Sethi, Geetika; Chopra, Gaurav; Samudrala, Ram (2015) Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Rev Med Chem 15:705-17
Hung, Ling-Hong; Samudrala, Ram (2014) fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data. Bioinformatics 30:1774-6
Minie, Mark; Chopra, Gaurav; Sethi, Geetika et al. (2014) CANDO and the infinite drug discovery frontier. Drug Discov Today 19:1353-63
Matasci, Naim; Hung, Ling-Hong; Yan, Zhixiang et al. (2014) Data access for the 1,000 Plants (1KP) project. Gigascience 3:17
Herbeck, Joshua T; Mittler, John E; Gottlieb, Geoffrey S et al. (2014) An HIV epidemic model based on viral load dynamics: value in assessing empirical trends in HIV virulence and community viral load. PLoS Comput Biol 10:e1003673
Dobrowsky, Terrence M; Rabi, S Alireza; Nedellec, Rebecca et al. (2013) Adhesion and fusion efficiencies of human immunodeficiency virus type 1 (HIV-1) surface proteins. Sci Rep 3:3014

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