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Computational chemistry represents molecular structures as a numerical models and simulates their behavior with the equations of quantum and classical physics. Available programs enable scientists to easily generate and present molecular data including geometries, energies and associated properties (electronic, spectroscopic and bulk). The usual paradigm for displaying and manipulating these data is a table in which compounds are defined by individual rows and molecular properties (or descriptors) are defined by the associated columns. A QSAR attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these 'rules' can be used to evaluate new chemical entities.
A QSAR generally takes the form of a linear equation
Qsar
Biological Activity = Const + (C1 P1) + (C2 P2) + (C3 P3) + ..
where the parameters P1 through Pn are computed for each molecule in the series and the coefficients C1 through Cn are calculated by fitting variations in the parameters and the biological activity. Since these relationships are generally discovered through the application of advanced statistical techniques which might be very difficult to implement manually, so a tool that would do all this calculation and would be available freely would higly contribute to the field. I would not give a list of available tools available but would rather introduce you to a tool which has been developed by me. The main motivation behind developing the tool is to enable inexperienced users to get into the field and take up the work without any big pain to go through long tutorials.
EasyQSAR has a very friendly GUI that guides through every step and gives valuable tips to analyze the data. But one thing that should be remembered is that the tool only makes the analysis when you provide the data about the descriptors and activity. It does not generates the descriptors itself.

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contact: kuntal.bhusan@gmail.com
To download visit : http://bioinformatikz.googlepages.com/
or, send a request mail to kuntal.bhusan@gmail.com

Step by step working instruction to perform a QSAR analysis using EasyQSAR

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  • A QSAR is of the form Biological Activity = Const + (C1 P1) + (C2 P2) + (C3 P3) + .. where the parameters P1 through Pn are computed for each molecule in the series and the coefficients C1 through Cn are calculated by fitting variations in the parameters and the biological activity.
  • NOW these P1,P2,P3 and so on are the descriptor variables of the QSAR equation you would be interested.
  • Many tools are available for generating these descriptors. Refer this link for calculating them : click here
  • Once you know how to calculate the descriptors the next thing which you need to get the possible other STRUCTURALLY RELATED drugs (by searching a drug library) and get their activity on the target.This is where you generate the library. Note: This library must be large enough (choose at least 10.
  • Calculate the descriptors(choose as many descriptors as you can and make sure that the descriptors you choose are varied enough ) for each compound from your library. (Also calculate the descriptors for your drug and keep it aside for now.)
Prepare the input files as in the sample files ( You can test with the given sample files )
  • descriptor.csv : will contain the descriptor values for your library compounds
  • activity.csv : will contain the activity value for your library compounds
Replace the files with same name in the folder named QSAR generated automatically.
  • Now run the tool as per the instructions and generate the equation, the tool will guide you step by step for the analysis.
  • Choose 'Analyze test data' option which will ask you to enter the descriptor values for an unknown sample (this is your drug). Enter the descriptor values in the .csv file in the same order as you entered for the library structures.This will predict the activity value for your drug compond based on the QSAR equation generated.

To get a better idea you can have a look into these published works (links provided below) that used EasyQSAR for their work


  1. Computational validation of 3-ammonio-3-(4-oxido- 1H-imidazol-1-ium-5-yl) propane-1, 1-bis (olate) as a potent anti-tubercular drug against mt-MetAP click here
  2. Synthesis, antimicrobial and QSAR studies of some new thiadiazepine derivatives click here
  3. Designing and screening of potent inhibitor against inha reductase of mycobacterium tuberculosis: a computational approach click here
  4. Molecular modeling and qsar analysis to explore therapeutic potentials of phytoestrogens in osteoporosis click here
  5. Resveratrol A Potent Angiotensin Converting Enzyme Inhibitor: AComputational Study in Relevance to Cardioprotective Activity. click here
  6. In-silico screening of potential inhibitors of gamma-secretase, a key enzyme of Alzheimer’s disease. click here
  7. Quantitative-Structure Activity Relationship (QSAR) Model for Abuse-liability Evaluation of Designer Drugs. click here
  8. Docking and QSAR based screening of some naturally occurring diterpenes as inhibitors of angiotensin converting enzyme (ACE) against cardiovascular diseases. click here
  9. An in silico insight into molecular mechanism of hypoglycemic activity of scoparic acid d, a diterpenoid from scoparia dulcis. click here

Developed by:
Kuntal Kumar Bhusan

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M.Tech Bioinformatics

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University of Hyderabad

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Reference to some of the content above is taken from :
Introduction to QSAR Methodolog
by AB Richon