The protein structure optimizer minimizes the energy of a protein target using combination of steepest decent conjugate gradient minimization algorithm using Cornell‟s force field equation. Higher the number of cycles in steepest descent and conjugate gradient, better will be the optimization.
MCSimulatorTM is used to generate conformer using three different methodologies. MCSimulatorTM is used to generate conformer using three different methodologies.
ClashOptimizer module is used to remove steric clashes in protein structures based on phi and psi angles. This tool helps in optimizing protein structure for Drug Discovery research.
Physico Chemical Features
Volume:Calculates volume of protein, ligand, protein-ligand complex.Gives total volume, individual volume and volume ratio.
Radius of Gyration:Calculates radius of gyration of protein, ligand & protein-ligand complex.
Energy:Calculates Energy of protein target
Surface Area:Calculates surface area of protein target by selecting chain.
Chain Donor Acceptors:Calculates side chain donor acceptors of protein target.
Secondary Structure:Calculates secondary structure information of protein target
SumperImpose:Superimpose chains/proteins over other.
Module for calculating binding energy of protein-ligand complex
HitsGen is a predictive tool of Inventus for calculating out binding energy of protein-ligand complexes.
For calculating binding energy of protein-ligand complex, two different methodologies are used. User can selected any methodology of them for calculating energy.
PocketDetector is a predictive tool of Inventus for discovering active site in protein targets. Active sites are given in ranking order and used as per user analysis
Module for virtual high throughput screening.
HitsGen is a predictive tool of Inventus for undergoing virtual high throughput screening.
NovoDocker is a tool of Inventus for undergoing docking studies. Selection of protein target file and compound is necessary..
Developed in collaboration with five major pharmaceutical
companies, the patented Absorption Model in PharmacoPredicta
predicts human intestinal absorption. The system's patented
dispersed plug flow model of absorption simulates human
physiology and accounts for the regional solubility, regional
permeability, intestinal surface area, and fluid flow in the
The PharmacoPredicta physiological Metabolism Model was
designed and validated to predict the first pass metabolism and
bioavailability (FH) of potential drug compounds. The parallel
tube liver flow model simulates first pass metabolism using a
predicted absorption rate from the Absorption Model, protein
binding, and metabolic stability of a compound. The Metabolism
Model was optimized using a training set of internally generated
in vitro data, literature and collaborator pharmacokinetic clinical
data, and chemical structures.
Based on the Distribution and Elimination Model published by
Kawai, et. al. (J. Pharm and Biopharm Vol. 22 No.5 1994), the
Distribution and Elimination Model in PharmacoPredicta uses
published human physiological blood flow rates and organ and
tissue volumes to predict the plasma level time curve (PLTC),
Cmax, tmax, and area under the curve (AUC) of a compound.
The physiological models in PharmacoPredicta employ an
embedded chemical structure-based model that can be used to
make ADME predictions early in drug discovery before in vitro
data has been generated.
The models of PharmacoPredicta are meant to be used iteratively
throughout the discovery process to refine predictions as the
structure-predicted values are replaced by in vitro