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.
The objective of this research was to examine the capabilities of QSPR (Quantitative Structure Property Relationship) modeling to predict specific biological responses (fibrinogen adsorption, cell attachment and cell proliferation index) on thin films of different polymethacrylates. Using 33 commercially available monomers it is theoretically possible to construct a library of over 40,000 distinct polymer compositions. A subset of these polymers were synthesized and solvent cast surfaces were prepared in 96 well plates for the measurement of fibrinogen adsorption. NIH 3T3 cell attachment and proliferation index were measured on spin coated thin films of these polymers. Based on the experimental results of these polymers, separate models were built for homo-, co-, and terpolymers in the library with good correlation between experiment and predicted values. The ability to predict biological responses by simple QSPR models for large numbers of polymers has important implications in designing biomaterials for specific biological or medical applications.
We performed molecular dynamics simulations of the low-molecular weight organic glass former ortho-terphenyl in bulk and freestanding films. The main motivation is to provide molecular insight into the confinement effect without explicit interfaces. Based on earlier models of ortho-terphenyl we developed an atomistic model for bulk simulations. The model reproduces literature data both from simulations and experiments starting from specific volume and diffusivity to mean square displacement and radial distribution functions. After characterizing the bulk model we form freestanding films by the elongation and expansion method. These films give us the opportunity to study the dynamical heterogeneity near the glass transition through in-plane mobility and reorientation dynamics. We finally compare the model in bulk and under confinement. We found qualitatively a lower glass transition temperature for the freestanding film compared to the bulk.
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