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VLifeAutoQSAR: Automated approach for swift activity prediction and efficient database search
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AutoQSAR is an advanced, rapid and intelligent QSAR solution from VLife that provides an approach to build a consensus QSAR model by presenting the best possible statistical models with a ‘single click’. The smart AutoQSAR approach drastically reduces time otherwise spent on iterative statistical model building and analysis especially when dealing with a large amount of molecular structural data along with their experimental properties or activities.

AutoQSAR workflow:
A typical approach using conventional QSAR requires significant statistical expertise and time as it involves a series of steps including property calculations, training and test set definition, variable selection and finally model generation. These steps usually have to be performed in several iterations before a ‘good’ QSAR model can be generated.

AutoQSAR simplifies this task by completely automating this procedure. AutoQSAR works in two simple steps:

Step 1: Input data set of molecules and their activity information

Step 2: Automatic QSAR process to produce the best model(s)

The AutoQSAR output is dictated by multiple options for variable selection and model building exercised by the user in one single instance.
Other core technologies from VLife stable
GQSAR: A patent pending technology for fragment based QSAR developed by VLife which enhances use of QSAR for design optimization of molecule delivering highly specific site directed clues for design modification.
VLifeSCOPE: A novel technology application creating a hybrid approach for lead optimization and prioritization of design for a given purpose from a library of molecules.
kNN-MFA: A novel combination of k-nearest neighbor method with molecular field analysis which takes cognizance of critical non-linear relationships between molecular properties with its activity.
LeadGrow+: An extension to the combinatorial library generation capability of VLifeMDS that significantly expands the chemical universe by enabling template substitution.
Aakar: A powerful and fast alignment independent shape search method with or without taking into consideration the chemical pharmacophoric features.
VLifeWorkFlow: A tool to customize and automate the discovery protocols of users using the CADD components.
VLife AutoQSAR
Advantages of VLifeAutoQSAR
Consensus approach to minimize repetitive model generation
One single consolidated report of best QSAR model(s) that can be used to screen a library
Powerful graphical representation enabling easy interpretation of results
Contribution plot of descriptors in the final model(s) to decide their relative importance in design of new molecules
Easy validation of the generated consensus model
Fitness plot to show the prediction accuracy of training and test set
Variety of statistical measures to determine confidence on QSAR model
Statistical parameters like r², pred r², q², Standard Error, F-test etc. to improve confidence on the generated model
Case studies: VLife technologies
Dual active antifungals Development of dual active antifungals View
Anti-cancer : AKT1 QSAR model Anti-cancer : AKT1 QSAR model development View
Pharmacophore identification and lead optimization Pharmacophore identification and lead optimization for novel antifungals View
"This new QSAR methodology gives QSARpro, a decisive edge over conventional QSAR. The ability to combine kNN with MFA is a unique approach which I came across only in QSARpro from VLife. It is now a method of choice in my research."

Dr. S.P.Gupta
Ex-BITS, Pilani
Core technologies
For site specific design clues
Lead optimization beyond docking
Taking cognizance of non-linearity
Benefit of scaffold hopping
Automated consesus based QSAR
Shape Based Screening
VLife WorkFlow
Customized workflow protocols
Functional technologies
Molecule visualization
VLife Engine
Conformer, force field analysis
Homology modeling & analysis
Protein - Ligand docking
Pharmacophore generation
Combinatorial library generation
Database searches
Property visualization
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