| |
|
| Technology > VLifeAutoQSAR |
|
|
|
|
|
|
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. |
| |
 |
| |
|
| 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. |
|
|
 |
| Enlarge |
|
|
|
One single consolidated report of best QSAR model(s) that can be used to screen a library |
Contribution plot of descriptors in the final model(s) to decide their relative importance in design of new molecules |
Fitness plot to show the prediction accuracy of training and test set |
Statistical parameters like r², pred r², q², Standard Error, F-test etc. to improve confidence on the generated model |
|
|
|
|
| |
|
 |
Development of dual active antifungals View |
 |
Anti-cancer : AKT1 QSAR model development View |
 |
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 |
|
|
| GQSAR |
| For site specific design clues |
|
|
|
| kNN-MFA |
| Taking cognizance of non-linearity |
|
|
|
|
| Automated consesus based QSAR |
|
|
| Aakar |
| Shape Based Screening |
|
|
|
|
|
|
|
|
|
|
|
|
| LeadGrow |
| Combinatorial library generation |
|
|
|
|
|
|
|
|
|
