3d Qsar Software Free Download

3d Qsar software, free download Windows 10
3d Qsar Software
Free Qsar Software

Easy-to-use yet powerful graphical interface:
A new, intuitive interface designed by UX experts working closely with our users provides powerful yet easy-to-use access to setup, execute, and analyze pharmacophore modeling experiments. A user can move seamlessly between hypotheses creation from protein-ligand complexes or only ligands, validation, modification, and screening with expert-level control as desired.

Universally applicable - Create Hypotheses from one or more ligands, protein-ligand complexes, and apo proteins:
Phase is well suited to drug discovery projects with and without receptor structures. Create hypotheses from protein-ligand complexes and apo proteins with Schrödinger's unique e-Pharmacophores technology or via observed interactions. For ligand based projects create hypotheses through common pharmacophore perception, from aligned known actives/inactives, or from particular ligands. Selectively merge hypotheses features from protein-ligand complexes and ligand-only to create hybrid models. Add your own features to hypothesis for complete control.

Qsar free download. The Chemistry Development Kit The Chemistry Development Kit (CDK) is a scientific, LGPL-ed library for bio- and cheminformatics an. 3D Rendering (1) Office/Business (1) Knowledge Management (1) Software Development (1) License License. Club-Caddie GMS (Golf Management Software) is a comprehensive software suite that. An open-source software aimed at unsupervised molecular alignment. Paolo Tosco, Thomas Balle b. A Department of Drug Science and Technology, University of Turin, via Pietro Giuria 9, 10125 Torino, Italy b Faculty of Pharmacy, University of Sydney, Pharmacy Building (A15), Camperdown Campus, Sydney NSW.

A unique common pharmacophore perception algorithm designed for use in both lead optimization and virtual screening:
Phase employs a newly developed common pharmacophore perception algorithm that flips the old paradigm by identifying ligand alignments first and then perceiving hypothesis. Using pharmacophore-based shape alignments, It quickly creates high-quality hypothesis from a handful to hundreds of known active ligands. A new scoring function, PhaseHypoScore, rank-orders hypotheses by their likely performance in virtual screening as well as the quality of ligand alignment. Easily recognize multiple binding modes in hypotheses from common pharmacophore perception when training against diverse known actives.

Many opportunities to introduce experimental data or user preferences:
While Phase can be used “out of the box' to quickly design and execute pharmacophore modeling experiments, it also allows users the option to exercise precise control over job settings at all steps, including pharmacophore creation, and screening. This enables users to fine-tune hypotheses creation and screening to bias results toward experimental observables.

Flexible creation and application of compound databases:
Phase uses Schrödinger's ConfGen and Epik for rapid and thorough sampling of conformational, ionization, and tautomeric space, with optional minimization using the best-in-class OPLS3 force field. Generation and updating of Phase databases can be efficiently distributed over all available computational resources. Phase databases can be used with all Schrödinger virtual screening methods including Glide, Phase, and Shape Screening. Multiple databases can be used in a single Phase screening calculation.

Fully prepared databases of purchasable compounds from Enamine, MilliporeSigma, and MolPort:
Schrödinger has partnered with Enamine, MilliporeSigma, and MolPort to provide a Phase database of fragments, lead-like, near drug-like, and drug-like compounds available from Enamine's 'Stock Screening Compounds Collection', MilliporeSigma's 'Aldrich Market Direct', and MolPort's 'Screening Compound Database' respectively. The databases can be updated quarterly to ensure compound availability and enable out-of-the-box virtual screening. Top-ranked compounds from a virtual screen can be easily purchased by ID directly from the compound vendors.

Citations and Acknowledgements

Schrödinger Release 2020-4: Phase, Schrödinger, LLC, New York, NY, 2020.

ö Dixon, S.L.; Smondyrev, A.M.; Knoll, E.H.; Rao, S.N.; Shaw, D.E.; Friesner, R.A., 'PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and 3D Database Screening. 1. Methodology and Preliminary Results,' J. Comput. Aided Mol. Des., 2006, 20, 647-671

ö Dixon, S.L.; Smondyrev, A.M.; Rao, S.N., 'PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching,' Chem. Biol. Drug Des., 2006, 67, 370-372

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Ask a Technical Expert

Got a question about our research model? Want to give us feedback? Contact a technical expert about TEST.

The Toxicity Estimation Software Tool (TEST) was developed to allow users to easily estimate the toxicity of chemicals using Quantitative Structure Activity Relationships (QSARs) methodologies. QSARs are mathematical models used to predict measures of toxicity from the physical characteristics of the structure of chemicals (known as molecular descriptors). Simple QSAR models calculate the toxicity of chemicals using a simple linear function of molecular descriptors:

Toxicity = ax₁ + bx₂ + c

Software Disclaimer

TEST estimates the toxicity values and physical properties of organic chemicals based on the molecular structure of the organic chemical entered by the user. The United States Environmental Protection Agency (U.S. EPA) makes no warranty, expressed or implied, as to the merchantability of TEST or its fitness for a particular purpose. Furthermore, the US EPA makes no claims concerning the accuracy of the data provided by TEST or its reliability for any purpose.

where x₁ and x₂ are the independent descriptor variables and a, b, and c are fitted parameters. The molecular weight and the octanol-water partition coefficient are examples of molecular descriptors. Additional examples are provided in our Molecular Descriptors Guide Version 1.0.2.

TEST allows a user to estimate toxicity without requiring any external programs. Users input a chemical to evaluate by drawing it in an included chemical sketcher window, entering a structure text file, or importing it from an included database of structures. Once entered, the toxicity is estimated using one of several advanced QSAR methodologies. The required molecular descriptors are calculated within TEST.

QSAR Methodologies

Several QSAR methodologies have been developed:

Hierarchical method – The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm-based technique is used to generate models for each cluster. The models are generated prior to runtime.
FDA method – The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
Single-model method – Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm-based approach. The regression model is generated prior to runtime.
Group contribution method – Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
Nearest neighbor method – The predicted toxicity is estimated by taking an average of the three chemicals in the training set that are most similar to the test chemical.
Consensus method – The predicted toxicity is estimated by taking an average of the predicted toxicities from each of the above QSAR methodologies.
Mode of action method - The predicted toxicity is calculated using a two-step process: (1) linear discriminant models are used to predict the aquatic toxicity mode of action and (2) the quantitative toxicity is predicted using the multiple linear regression model developed for that mode of action.

These methodologies are explained in detail in the publications below.

The software includes models for the following endpoints:

3d Qsar software, free download Windows 10

96-hour fathead minnow 50 percent lethal concentration (LC50)
48-hour daphnia magna 50 percent lethal concentration (LC50)
Tetrahymena pyriformis 50 percent growth inhibition concentration (IGC50) Exit
Oral rat 50 percent lethal dose (LD50) Exit
Bioconcentration Factor (BCF) The bioconcentration factor data set was compiled by researchers at the Mario Negri Istituto Di Ricerche FarmacologicheExit
Developmental Toxicity (DevTox) Exit
Ames Mutagenicity (Mutagenicity) Exit

TEST is based on The Chemistry Development KitExit, an open-source Java library for computational chemistry.

The software now contains models for the following physical properties:

Surface tension @25
Viscosity @25C
Water solubility @25C
Thermal conductivity @25C
Vapor pressure @25C

Models for additional endpoints will be added as they are completed.

What's new in Version 4.2.1?

Corrected bug where FDA method was omitted from the list of method options.

Top of Page

Prior Version History

4.2 (4/2016)
- Added MOA based method for calculating acute fathead minnow toxicity
- Fixed bug involving selecting the output folder
- Fix inconsistencies in the calculation of the ALOGP descriptor
4.1 (7/27/2012)
- Results are now displayed for the most similar chemicals in the training and test sets (enables users to assess confidence in the predicted value)
  - The results pages now list which fragment is missing if the fragment constraint is violated
  - Fixed bug which occurred when saving results files to network drives
  - Fixed bug that occurred when editing chemicals in the batch list
  - Fixed bug where single model method was not included for batch mode predictions
  - Added the ability to copy the smiles of the current structure to the clipboard
  - Added the ability to load recently analyzed structures from the File menu
  - Added the ability to load recently generated batch results files from the File menu
  - Improved the speed of loading large aromatic compounds from MDL SD files
  - Updated/added endpoints
4.0 (6/7/11)
- Physical properties are now estimated
- Batch mode is improved:
  - Loading can now be interrupted
  - Chemicals with loading errors are displayed at the top of the batch table
  - Can now load SMILES files with no identifier field (chemicals are assigned arbitrary IDs)
- Aromaticity detection is improved:
  - Can handle aromatic bond orders (bond order = 4) in mol or sd files
  - The SMILES parser has been improved to better handle complicated aromatic ring systems
- Added Options screen:
  - Added ability to change the output directory after it has been set
  - The program now remembers the previously selected output folder
  - The 'Relax fragment constraint' checkbox was moved to Options screen
3.3 (7/8/10)
- Daphnia magna LC50 endpoint was added
- AMES Mutagenicity endpoint was added
- The following changes were made for binary endpoints such as developmental toxicity and AMES mutagenicity:
  - QSAR models now have stricter statistical standards (leave one out concordance = 0.8, sensitivity = 0.5, and specificity = 0.5)
  - Model statistics such as concordance, sensitivity, and specificity are now displayed in the results web pages
3.2 (12/18/09)
- Reproductive toxicity endpoint was added
- Random forest QSAR method was added (for reproductive toxicity endpoint only)
3.1 (6/23/09)
- Fixed issue with running TEST in non-english speaking countries
3.0 (4/14/09)
- Random selection is used to divide the data sets into training and test sets
- Added BCF endpoint
- Added consensus prediction method
2.0 (2/24/09)
- Each toxicity data set is now split into a training and test set.
- The toxicity models included in the software are now fit to the training sets (previously they were fit to the overall sets)
- The batch mode was improved (chemicals can be added and the list can now be saved as an SDF)
1.0.3 (10/24/08)
- Fixed calculation of 'ieadje' molecular descriptor
- Fixed definitions of chi descriptors in numbered list in molecular descriptors guide

System Requirements

In Version 4.2, a separate copy of Java will be installed which should reduce compatibility issues.
Two or more GB of RAM is recommended.

Installation Instructions

Save the appropriate installation file to your hard drive. Due to the large size of the file, the download may take 15 minutes or longer depending on the speed of the connection.
Double-click the installation file (for Linux users: open a shell, cd to the directory where you downloaded the installer and at the prompt type: sh ./install.bin).

Silent Installation Instructions for Network Administrators (for Windows users)

The software can be installed silently by issuing the following command at the command prompt: install -i silent

Download TEST (version 4.2.1)

3d Qsar Software

TEST for Windows with Automatic Installation (EXE)(298 MB)
TEST for MacOS (ZIP)(307 MB)
TEST for Linux (ZIP)(309 MB, August 2016)

Training and prediction sets(12 MB) used in T.E.S.T. (sdf format)

Structure Data Files (ZIP)(3 K) (such as a MDL SD file).

Free Qsar Software

Publications

U.S. EPA (2016). 'User’s Guide for T.E.S.T. (version 4.2) (Toxicity Estimation Software Tool): A Program to Estimate Toxicity from Molecular Structure.'

Sushko, I.; Novotarskyi1, S.; Körner, R.; Pandey, A. K.; Cherkasov, A.; Li, J.; Gramatica, P.; Hansen, K.; Schroeter, T.; Müller, K.-R.; Xi, L.; Liu, H; Yao, X.; Öberg, T.; Hormozdiari, F.; Dao, F.; Sahinalp, C.; Todeschini, R.; Polishchuk, P.; Artemenko, A.; Kuz’min, V.; Martin, T.M.; Young, D. M.; Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.; Varnek, A; Prokopenko, V. V.; Tetko, I.V. (2010). “Applicability domains for classification problems: benchmarking of distance to models for AMES mutagenicity set.” J. Chem. Inf. Model, 50, 2094-2111.

Cassano, A.; Manganaro, A; Martin, T.; Young, D.; Piclin, N.; Pintore, M.; Bigoni, D.; Benfenati, E. (2010). “The CAESAR models for developmental toxicity.” Chemistry Central Journal, 4(Suppl 1):S4.

Zhu, H.; Martin, T.M.; Young, D. M.; Tropsha, A. (2009). “Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure.“ Chemical Research in Toxicology, 22 (12), pp 1913-1921.

Benfenati, E., Benigni, R., Demarini, D.M., Helma, C., Kirkland, D., Martin, T.M., Mazzatorta, G., Ouedraogo-Arras, G., Richard, A.M., Schilter, B., Schoonen, W.G.E.J., Snyder, R.D., and C. Yang. (2009). “Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives.” Journal of Environmental Science and Health Part C, 27, 2: 57-90.

Young, D.M.; Martin, T.M.; Venkatapathy, R.; Harten, P. (2008) “Are the Chemical Structures in your QSAR Correct?” QSAR & Combinatorial Science, 27 (11-12), 1337-1345.

Martin,T.M., P. Harten, R. Venkatapathy, S. Das and D.M. Young. (2008). “A Hierarchical Clustering Methodology for the Estimation of Toxicity.” Toxicology Mechanisms and Methods, 18, 2: 251–266.

Martin, T.M., and D.M. Young. (2001). “Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds in the Fathead Minnow (Pimephales Promelas) Using a Group Contribution Method.” Chemical Research in Toxicology, 14, 10: 1378–1385.