The need for developing rigorous QSAR modeling protocols, reliable models and accurate activity/property predictors has never been more important. This need is illustrated by two major examples of recent public efforts spearheaded both by the US and European community in the areas of bioactivity prediction (PubChem) and environmental safety (OECD Programme), respectively. To address this critical need, the overarching goal of our research is to develop a universally applicable and robust predictive QSAR modeling framework that will afford highly significant, externally validated, and predictive QSAR models of important biological endpoints. The critical components of such framework have been developed in the course of many years of our research on QSAR methodology development and application to experimental datasets. Building upon our previous experience, this proposal focuses on the design of optimized QSAR protocols for the development of reliable models (or predictors) of multiple target datasets that are useful for the virtual screening and accurate prediction of the target activities or properties for large databases or virtual libraries of untested chemical entities. These highly intertwined objectives will be achieved via concurrent development of novel QSAR methodologies (Specific Aim 1), application of these methodologies to multiple available datasets of biologically active compounds, especially of complex nature, to develop validated and predictive target-specific models, or predictors (Specific Aim 2), and virtual functional annotation of existing chemical databases (Specific Aim 3). As has always been characteristic of our research, we intend to make all our algorithms, predictors, and annotated compound databases publicly available via the C-ChemBench [ceccr.unc.edu] system that is being developed in our group with the support from the previous cycle as well as with additional funding (Center planning grant) provided by the NIH RoadMap program. We expect that the implementation of this project will advance the field of chemical genomics by developing the highly robust and publicly available predictive QSAR modeling framework, multiple validated models of diverse biologically significant endpoints, and multiple candidate compound hit lists prioritized for biological testing against the selected endpoints. PUBLIC HEALTHE

Public Health Relevance

The need for developing robust Quantitative Structure Activity (QSAR) methodologies is illustrated by two major examples of recent public efforts spearheaded both by the US and European community in the areas of bioactivity prediction (PubChem) and environmental safety (OECD Programme), respectively. To address this critical need, the overarching goal of our research is to develop a universally applicable and robust predictive QSAR modeling framework that will afford highly significant, externally validated, and predictive QSAR models of important biological endpoints.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM066940-08
Application #
8092546
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2003-07-01
Project End
2013-05-31
Budget Start
2011-06-01
Budget End
2013-05-31
Support Year
8
Fiscal Year
2011
Total Cost
$284,418
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Capuzzi, Stephen J; Kim, Ian Sang-June; Lam, Wai In et al. (2017) Chembench: A Publicly Accessible, Integrated Cheminformatics Portal. J Chem Inf Model 57:105-108
Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A et al. (2016) QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 13:545-56
Dekina, Svetlana; Romanovska, Irina; Ovsepyan, Ani et al. (2016) Gelatin/carboxymethyl cellulose mucoadhesive films with lysozyme: Development and characterization. Carbohydr Polym 147:208-15
Neves, Bruno Junior; Muratov, Eugene; Machado, Renato Beilner et al. (2016) Modern approaches to accelerate discovery of new antischistosomal drugs. Expert Opin Drug Discov 11:557-67
Fourches, Denis; Muratov, Eugene; Tropsha, Alexander (2016) Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J Chem Inf Model 56:1243-52
Melo-Filho, Cleber C; Dantas, Rafael F; Braga, Rodolpho C et al. (2016) QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni. J Chem Inf Model 56:1357-72
Fourches, Denis; Pu, Dongqiuye; Li, Liwen et al. (2016) Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. Nanotoxicology 10:374-83
Wambaugh, John F; Wetmore, Barbara A; Pearce, Robert et al. (2015) Toxicokinetic Triage for Environmental Chemicals. Toxicol Sci 147:55-67
Alves, Vinicius M; Muratov, Eugene; Fourches, Denis et al. (2015) Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacol 284:262-72
Braga, Rodolpho C; Alves, Vinicius M; Silva, Meryck F B et al. (2015) Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity. Mol Inform 34:698-701

Showing the most recent 10 out of 81 publications