%0 Journal Article
%T DockScreen: A Database of In Silico Biomolecular Interactions to Support Computational Toxicology
%A Michael-Rock Goldsmith
%A Christopher M. Grulke
%A Daniel T. Chang
%A Thomas R. Transue
%A Stephen B. Little
%A James R. Rabinowitz
%A Rogelio Tornero-Velez
%J Dataset Papers in Science
%D 2014
%R 10.1155/2014/421693
%X We have developed DockScreen, a database of in silico biomolecular interactions designed to enable rational molecular toxicological insight within a computational toxicology framework. This database is composed of chemical/target (receptor and enzyme) binding scores calculated by molecular docking of more than 1000 chemicals into 150 protein targets and contains nearly 135 thousand unique ligand/target binding scores. Obtaining this dataset was achieved using eHiTS (Simbiosys Inc.), a fragment-based molecular docking approach with an exhaustive search algorithm, on a heterogeneous distributed high-performance computing framework. The chemical landscape covered in DockScreen comprises selected environmental and therapeutic chemicals. The target landscape covered in DockScreen was selected based on the availability of high-quality crystal structures that covered the assay space of phase I ToxCast in vitro assays. This in silico data provides continuous information that establishes a means for quantitatively comparing, on a structural biophysical basis, a chemical¡¯s profile of biomolecular interactions. The combined minimum-score chemical/target matrix is provided. 1. Introduction A major challenge with chemicals in consumer products, including but not limited to both pharmaceutical and environmental chemicals, is the ability to fully discover, characterize, and anticipate adverse effects that may result as a consequence of exposure to these chemicals. Classical safety assessment and animal studies are not only cost-prohibitive and lengthy [1, 2]; they often do not include the data required for extrapolations that are inherent in human risk assessment [3]. Developing and evaluating predictive strategies to elucidate the mode of biological activity of environmental chemicals are major undertaking of the US Environmental Protection Agency¡¯s Computational Toxicology program (http://www.epa.gov/comptox/). Aligning these strategies with the Agency¡¯s ongoing chemical-specific risk assessment needs provision of additional incentive to develop new means of elucidating key determinants of toxicity in the chemical source-to-outcome continuum at a molecular level of accountability. This has provided the motivation for the development of tools such as the Aggregated Computational Toxicology Resource (http://www.epa.gov/actor) [1], the DSSTox Toxico-Chemoinformatics initiative (http://www.epa.gov/ncct/dsstox/) [4], and the ToxRefDB (http://www.epa.gov/ncct/toxrefdb/) [5] in vivo animal effects database. In an attempt to fill the inherently large data gaps required for
%U http://www.hindawi.com/journals/dpis/2014/421693/