Chemical Computing Group

Montréal, Canada

Chemical Computing Group

Montréal, Canada
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This report studies the Global Biosimulation Market, analyzes and researches the Biosimulation development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like Certara Simulation Plus Dassault Systèmes Schr?dinger Advanced Chemistry Development Chemical Computing Group Entelos Genedata Ag Physiomics PLC Rhenovia Pharma Market segment by Application, Biosimulation can be split into Application 1 Application 2 Application 3 United States, EU, Japan, China, India and Southeast Asia Biosimulation Market Size, Status and Forecast 2021 1 Industry Overview of Biosimulation 1.1 Biosimulation Market Overview 1.1.1 Biosimulation Product Scope 1.1.2 Market Status and Outlook 1.2 Global Biosimulation Market Size and Analysis by Regions 1.2.1 United States 1.2.2 EU 1.2.3 Japan 1.2.4 China 1.2.5 India 1.2.6 Southeast Asia 1.3 Biosimulation Market by End Users/Application 1.3.1 Application 1 1.3.2 Application 2 1.3.3 Application 3 2 Global Biosimulation Competition Analysis by Players 2.1 Biosimulation Market Size (Value) by Players (2015-2016) 2.2 Competitive Status and Trend 2.2.1 Market Concentration Rate 2.2.2 Product/Service Differences 2.2.3 New Entrants 2.2.4 The Technology Trends in Future 3 Company (Top Players) Profiles 3.1 Certara 3.1.1 Company Profile 3.1.2 Main Business/Business Overview 3.1.3 Products, Services and Solutions 3.1.4 Biosimulation Revenue (Value) (2011-2016) 3.1.5 Recent Developments 3.2 Simulation Plus 3.2.1 Company Profile 3.2.2 Main Business/Business Overview 3.2.3 Products, Services and Solutions 3.2.4 Biosimulation Revenue (Value) (2011-2016) 3.2.5 Recent Developments 3.3 Dassault Systèmes 3.3.1 Company Profile 3.3.2 Main Business/Business Overview 3.3.3 Products, Services and Solutions 3.3.4 Biosimulation Revenue (Value) (2011-2016) 3.3.5 Recent Developments 3.4 Schr?dinger 3.4.1 Company Profile 3.4.2 Main Business/Business Overview 3.4.3 Products, Services and Solutions 3.4.4 Biosimulation Revenue (Value) (2011-2016) 3.4.5 Recent Developments 3.5 Advanced Chemistry Development 3.5.1 Company Profile 3.5.2 Main Business/Business Overview 3.5.3 Products, Services and Solutions 3.5.4 Biosimulation Revenue (Value) (2011-2016) 3.5.5 Recent Developments 3.6 Chemical Computing Group 3.6.1 Company Profile 3.6.2 Main Business/Business Overview 3.6.3 Products, Services and Solutions 3.6.4 Biosimulation Revenue (Value) (2011-2016) 3.6.5 Recent Developments 3.7 Entelos 3.7.1 Company Profile 3.7.2 Main Business/Business Overview 3.7.3 Products, Services and Solutions 3.7.4 Biosimulation Revenue (Value) (2011-2016) 3.7.5 Recent Developments 3.8 Genedata Ag 3.8.1 Company Profile 3.8.2 Main Business/Business Overview 3.8.3 Products, Services and Solutions 3.8.4 Biosimulation Revenue (Value) (2011-2016) 3.8.5 Recent Developments 3.9 Physiomics PLC 3.9.1 Company Profile 3.9.2 Main Business/Business Overview 3.9.3 Products, Services and Solutions 3.9.4 Biosimulation Revenue (Value) (2011-2016) 3.9.5 Recent Developments 3.10 Rhenovia Pharma 3.10.1 Company Profile 3.10.2 Main Business/Business Overview 3.10.3 Products, Services and Solutions 3.10.4 Biosimulation Revenue (Value) (2011-2016) 3.10.5 Recent Developments For more information or any query mail at [email protected]


Wright J.S.,Carleton University | Anderson J.M.,Carleton University | Shadnia H.,Chemical Computing Group | Durst T.,University of Ottawa | Katzenellenbogen J.A.,Urbana University
Journal of Computer-Aided Molecular Design | Year: 2013

The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4-7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities. © 2013 Springer Science+Business Media Dordrecht.


Isaacs K.K.,U.S. Environmental Protection Agency | Glen W.G.,Alion Science and Technology Corporation | Egeghy P.,U.S. Environmental Protection Agency | Goldsmith M.-R.,Chemical Computing Group | And 5 more authors.
Environmental Science and Technology | Year: 2014

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (p < 0.0001, R2 = 0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential. © 2014 American Chemical Society.


Isaacs K.K.,U.S. Environmental Protection Agency | Goldsmith M.-R.,Chemical Computing Group | Egeghy P.,U.S. Environmental Protection Agency | Phillips K.,Oak Ridge Institute for Science and Education | And 3 more authors.
Toxicology Reports | Year: 2016

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization. © 2016


PubMed | U.S. Environmental Protection Agency, Duke University, Chemical Computing Group and Oak Ridge Institute for Science and Education
Type: Journal Article | Journal: Environmental science & technology | Year: 2016

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.


Corbeil C.R.,Chemical Computing Group | Williams C.I.,Chemical Computing Group | Labute P.,Chemical Computing Group
Journal of Computer-Aided Molecular Design | Year: 2012

The results of cognate docking with the prepared Astex dataset provided by the organizers of the "Docking and Scoring: A Review of Docking Programs" session at the 241st ACS national meeting are presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80 % of the Astex targets, the MOE docker produces a top-scoring pose within 2 Å of the X-ray structure. For 91 % of the targets a pose within 2 Å of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 Å from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A "dataset preparation" error of 0.5 kcal/mol is shown to cause fluctuations of over 20 % in docking success rates. © The Author(s) 2012.


Feher M.,Campbell University | Williams C.I.,Chemical Computing Group
Journal of Chemical Information and Modeling | Year: 2012

This work examines the sensitivity of docking programs to tiny changes in ligand input files. The results show that nearly identical ligand input structures can produce dramatically different top-scoring docked poses. Even changing the atom order in a ligand input file can produce significantly different poses and scores. In well-behaved cases the docking variations are small and follow a normal distribution around a central pose and score, but in many cases the variations are large and reflect wildly different top scores and binding modes. The docking variations are characterized by statistical methods, and the sensitivity of high-throughput and more precise docking methods are compared. The results demonstrate that part of docking variation is due to numerical sensitivity and potentially chaotic effects in current docking algorithms and not solely due to incomplete ligand conformation and pose searching. These results have major implications for the way docking is currently used for pose prediction, ranking, and virtual screening. © 2012 American Chemical Society.


Feher M.,Campbell University | Williams C.I.,Chemical Computing Group
Journal of Chemical Information and Modeling | Year: 2012

This work examines the effect of small input perturbations on binding energies computed from differences between energy minimized structures, such as the Prime MM-GBSA and MOE MM-GB/VI methods. The applied perturbations include translations of the cognate ligand in the binding site by a maximum of 0.1 Å along each coordinate or the permutation of the order of atoms of the cognate ligand without any changes to the atom coordinates. These seemingly inconsequential input changes can lead to as much as 17 kcal/mol differences in the computed binding energy. The calculated binding energies cluster around discrete values, which correspond to specific ligand poses. It appears that the largest variations are observed for target-ligand systems in which there is a possibility for multiple poses with strong hydrogen bonds. The barriers between different poses can appear fractal-like, making it difficult to predict which solution will be produced from a given input. Including protein flexibility in MM-GBSA calculations further increases numerical instability, and the protein strain terms seem to be the major factor contributing to this sensitivity. In such calculations it appears unwise to extend the flexible region beyond 6 Å. © 2012 American Chemical Society.


Feher M.,Campbell University | Williams C.I.,Chemical Computing Group
Journal of Chemical Information and Modeling | Year: 2010

The variability of docking results as a function of variations in ligand input conformations was studied for the GOLD, Glide, FlexX, and Surflex programs. It is concluded that there are two major effects leading to such variability: the adequacy of conformational search during docking and random "chaotic" effects arising from sensitivity to small input perturbations. It is shown that although the former is generally the stronger effect, the latter is also highly significant for almost all docking engines. The strong target-to-target variation of the magnitude of these effects is emphasized. The performance of different packages is compared using these measures. Guidelines are provided for different programs to reduce variability and improve reproducibility, which involve using a small number of input conformations as starting points for docking, followed by the selection of the top scoring docked pose from the results as the best docked solution. © 2010 American Chemical Society.


PubMed | Chemical Computing Group
Type: Journal Article | Journal: Journal of computer-aided molecular design | Year: 2012

The results of cognate docking with the prepared Astex dataset provided by the organizers of the Docking and Scoring: A Review of Docking Programs session at the 241st ACS national meeting are presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80% of the Astex targets, the MOE docker produces a top-scoring pose within 2 of the X-ray structure. For 91% of the targets a pose within 2 of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A dataset preparation error of 0.5kcal/mol is shown to cause fluctuations of over 20% in docking success rates.

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