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kNN-MFA: Exploiting the contribution of non-linear relationships in QSAR
Technology > kNN-MFA
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kNN-MFA is a novel methodology. Unlike conventional QSAR regression methods, this methodology can handle non-linear relationships of molecular field descriptors with biological activity, thus making it a more accurate predictor of biological activity.

Conventional correlation methods try to generate linear relationship with the activity, whereas kNN is inherently non-linear method and is better able to explain activity trends.

kNN-MFA is built around the conceptually simple approach of pattern recognition working on active analog principle. The best part is that kNN-MFA has the ability to generate several models using user defined training and test set.
Other core technologies from VLife stable
GQSAR: A patent pending technology for fragment based QSAR developed by VLife that 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.
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.
Pub: J. Chem. Inf. Model. 46, 24-31,2006
Three-Dimensional QSAR Using the k-Nearest Neighbor Method and its Interpretation, Subhash Ajmani, Kamalakar Jadhav and Sudhir A. Kulkarni
Advantages of kNN-MFA technology
Inherent consideration for non-linearity
kNN MFA does not assume a linear relation between activity and molecular properties and the inherent non-linearity in the method leads to improved models resulting in better predictive ability
More accurate predictive ability
kNN MFA has an intrinsic approach of pattern recognition on active analog principle and its ability to exhaustively scan several possible models utilizing user defined selections on the data set
Contribution to more diverse library generation
The QSAR models derived with kNN MFA can lead to generation of a library of molecules that satisfy one or many design considerations suggested by kNN-MFA model
Case studies: VLife technologies
Regression technique A novel regression technique for modeling non-linear activity and property data View
Enzymes Application of enzymes to enhance drug action View
Target Identification Target Identification for existing nutraceutical molecule 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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