QSAR softwares

The FDA [51] has used the MDL QSAR software [19] to develop QSARs for the carcinogenic potential of pharmaceuticals and organic chemicals. These were validated using a test set of 108 compounds, with 72% correct prediction of carcinogens and 72% correct prediction of noncarcinogens. 

Contrera, J.F., Matthews, E.J., Kruhlak, N.L., Benz, R.D. (2005). In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL-QSAR software. Regul. Toxicol. Pharmacol. 43 313-23. 

OSAR. An analysis of the activity of oxime ethers was undertaken to further our understanding of the SAR in this system. All analyses were performed utilizing a FMC proprietary Quantitative Structure Activity Relationships (QSAR) software system. Parameters for structure-activity studies were obtained as previously... 

Most of the QSAR software listed in Box 23.3 is readily available in PC packages (e.g. IMP), or as client-server applications such as SAS and Pipeline Pilot. Some is freeware such as R or Orange. Unfortunately, much of the... 

In the calculations presented, heptachlor is degraded into heptachlorepoxide in all environmental media (with a fraction of formation ff = 0.9), aldrin is degraded into dieldrin in all environmental media (jf = 0.9), too, whereas DDT degrades into DDE in the atmosphere (jf = 0.9), and in equal parts (hothff= 0.5) into DDE and DDD in all the other media. Degradation half-lives were extracted as experimental values from the literature [37,38] where possible, or calculated with QSAR software (especially for OH reactions) [39]. 

COODESSA PRO, QSPR/QSAR SOFTWARE Comprehensive DEscriptors for Structural and Statistical Analysis) by A. R. Katritzky, M. Karelson, and R. Petrukhin, University of Florida, Gainesville, FL, 2001-2005. 

Studied compounds or where no structural data are available for a given target, such as when the desired effect is of a systemic nature. For this reason, QSAR appears to be the most common method of in silica drag discovery [27], MFTA [73], HIT QSAR [51], PASS [70], ISIDA [92], NASAWIN [8], and CORAL [89] are QSAR software packages that have been successfully utilized for the prediction of a wide variety of biological activities. 

MSA software available in Ceriiis-, Molecular Simulations, Inc., 9685 Scranton Rd., San Diego, CA 92121. Drug Discovery Workbench QSAR+ Software, Release 2.0 (1995). 

Pearlman R S and K M Smith 1998. Novel Software Tools for Chemical Diversity. Perspectives in Dn Discovery and Design vols 9/10/ll(3D QSAR in Drug Design Ligand/Protein Interactions ar Molecular Similarity), pp. 339-353. 

CODESSA reads molecular structure files or output files created by other software packages as the starting point for QSAR analysis. It can import computational results from AMPAC, MOPAC, and Gaussian as well as structures in a number of common formats. 

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. 

QSAR modeling. Therefore considerably larger and more consistent data sets for each enzyme will be required in future to increase the predictive scope of such models. The evaluation of any rule-based metabolite software with a diverse array of molecules will indicate that it is possible to generate many more metabolites than have been identified in the literature for the respective molecules to date, which could also reflect the sensitivity of analytical methods at the time of publishing the data. In such cases, efficient machine learning algorithms will be necessary to indicate which of the metabolites are relevant and will be likely to be observed under the given experimental conditions. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

There are software that use more approaches for the prediction of toxicity expert systems, QSAR, and read-across (http //www.insilico.eu/use-qsar.html). 

The first innovation introduced by the work is the approach used in the case of missing data human toxicological and ecotoxicological values were predicted using freely available QSAR models like Toxicity Estimation Software Tool (T.E.S.T.) v 3.2 [26] and ToxBoxes [27] (nowadays ToxBoxes is no longer free). 

ACD/Tox Suite is a collection of software modules that predict probabilities for basic toxicity endpoints. Predictions are made from chemical structure and based upon large validated databases and QSAR models, in combination with expert knowledge of organic chemistry and toxicology. ToxSuite modules for Acute Toxicity, Genotoxicity, Skin Irritation, and Aquatic Toxicity have been used. 

USEPA (2010) Toxicity Estimation Software Tool (version 3.2). Available at http //www.epa. gov/nrmri/std/cppb/qsar/... 

Cabrera et al. [50] modeled a set of 163 drugs using TOPS-MODE descriptors with a linear discriminant model to predict p-glycoprotein efflux. Model accuracy was 81% for the training set and 77.5% for a validation set of 40 molecules. A "combinatorial QSAR" approach was used by de Lima et al. [51] to test multiple model types (kNN, decision tree, binary QSAR, SVM) with multiple descriptor sets from various software packages (MolconnZ, Atom Pair, VoSurf, MOE) for the prediction of p-glycoprotein substrates for a dataset of 192 molecules. Best overall performance on a test set of 51 molecules was achieved with an SVM and AP or VolSurf descriptors (81% accuracy each). 

Thorium metal, 24 759-761 in alloys, 24 760-761 preparation of, 24 759-760 properties of, 24 760-761 reactions of, 24 761 Thorium nitrate, 24 757, 766 Thorium oxalates, 24 768-769 Thorium oxide, 21 491 Thorium oxides, 24 757, 761-762 Thorium oxyhalides, 24 762 Thorium perchlorate, 24 764 Thorium phosphates, 24 765-766 Thorium pnictides, 24 761 Thorium sulfate, 24 764 Thorium-uranium fuel cycle, 24 758-759 Thorocene, 24 772 Thorotrast, 24 775-776 3A zeolite. See Zeolite 3A Three-boiling beet sugar crystallization scheme, 23 463-465 Three-color photography, 19 233-234 3D models, advantages of, 19 520-521 3D physical design software, 19 519-521 3D QSAR models, 10 333. See also QSAR analysis... 

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