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GQSAR: Technology for fragment based QSAR
Technology > GQSAR
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GQSAR is a breakthrough patent pending methodology that significantly enhances the use of QSAR as an approach for new molecule design. As a predictive tool for activity, this method is significantly superior to conventional 3D and 2D QSAR.

In this method, every molecule of the data set is considered as a set of fragments, the fragmentation scheme being either template based or user defined.

The descriptors are evaluated for each fragment and a relationship between these fragment descriptors is formed with the activity of the whole molecule.

Unlike conventional QSAR, with the GQSAR, researchers get critically important site specific clues within a molecule where a particular descriptor needs to be modified.

GQSAR approach builds upon the basic focus of QSAR by applying the knowledge gained in the field over the past four decades in terms of molecular descriptors, statistical modeling etc.
 
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Other core technologies from VLife stable
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.
VLifeAutoQSAR: Unique automated approach to conduct QSAR that provides a best result based on a consensus of multiple QSAR models generated.
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.
 
GQSAR
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Pub: QSAR & Combinatorial Science 2009
Group Based QSAR(GQSAR): Mitigating Interpretation Challenges in QSAR, Subhash Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni
Advantages of GQSAR technology
Extends application of QSAR beyond just library screening
Provides very important site specific clues for better molecule design optimization
Better understanding of how a molecule imparts biological activity
Delivers understanding about the contribution of various parts of a molecule towards governing biological activity variation
Removes interpretation challenges in QSAR significantly
Offers the best of conventional QSAR approaches while mitigating interpretation challenges from such approaches
Extensive choice of fragmentation schemes for wide applicability
Choice of fragmentation schemes based on the nature of the data set to handle congeneric as well as non-congeneric series of molecules
 
Case studies: VLife technologies
QSAR technology A novel QSAR technology for new molecule design View
Dual active antifungals Development of dual active antifungals View
Anti-cancer : Classification model Anti-cancer : Classification model development 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
GQSAR
For site specific design clues
VLifeSCOPE
Lead optimization beyond docking
kNN-MFA
Taking cognizance of non-linearity
LeadGrow+
Benefit of scaffold hopping
VLifeAutoQSAR
Automated consesus based QSAR
Aakar
Shape Based Screening
VLife WorkFlow
Customized workflow protocols
Functional technologies
VLifeBase
Molecule visualization
VLife Engine
Conformer, force field analysis
ProModel
Homology modeling & analysis
VLifeDock
Protein - Ligand docking
VLifeQSAR
2D QSAR, 3D QSAR
MolSign
Pharmacophore generation
LeadGrow
Combinatorial library generation
ChemDBS
Database searches
ProViz
Property visualization
 
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