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Drug discovery and development is a costly and time-consuming endeavor (Calcoen et al. Nat Rev Drug Discov 14(3):161-162, 2015; The truly staggering cost of inventing new drugs. Forbes. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/, 2012; Scannell et al. Nat Rev Drug Discov 11(3):191-200, 2012). Over the last two decades, computational tools and in silico models to predict ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of molecules have been incorporated into the drug discovery process mainly in an effort to avoid late-stage failures due to poor pharmacokinetics and toxicity. It is now widely recognized that ADMET issues should be addressed as early as possible in drug discovery. Here, we describe in detail how ADMET models can be developed and applied using a commercially available package, ADMET Predictor™ 7.2 (ADMET Predictor v7.2. Simulations Plus, Inc., Lancaster, CA, USA).

Parameter optimization for chemical systems requires generation of initial guesses. These parameters should be generated using systematic sampling of parameter space, minimizing differences between output data and the corresponding reference data. In this paper we discuss the ParamChem project, which is creating reusable and extensible infrastructure for the computational chemistry community that will reduce unnecessary and eliminate redundancies in parametrized computations using modern software engineering tools.The paper particularly focuses on constructing and executing coupled molecular chemistry models as complicated workflow graphs. These workflow management capabilities have been integrated with the GridChem Science Gateway infrastructure through the TeraGrid advanced user support program. Further, we describe how the project is enabling a sustainable growth for science gateway infrastructure by building upon tools provided by the Open Gateway Computing Environments. The paper also discusses plans for integrating TeraGrid information, monitoring and prediction services to provide automated job scheduling with resource maintenance and fault aware services.

This review focuses on polymeric biomaterials and provides a selective overview of the computational modeling approaches used to predict their properties and biological responses. Also, a short overview of existing databases and software packages for the biomaterials field is presented. The review summarizes the research in this area since the year 2000.

alpha-carbon coordinates. The distance of each eluted peptide from its hull was then computed (d: distance of peptide to protein surface) and normalized against the distance from the center of the hull (S: percent submergence in source protein). In our preliminary survey, 53% of the samples had d-values of 1A or less, with 51% having S-values of less that 15%, indicating that peptides that are selected as MHCII binders are relatively near the surface of their undigested source proteins. Our preliminary results indicate that timing, as well as affinity, may be equally important in MHCII binding.