Protein Preparation

Energy Optimizer


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.

MonteCarlo Simulation


MCSimulatorTM is used to generate conformer using three different methodologies. MCSimulatorTM is used to generate conformer using three different methodologies.

Clash Optimizer


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.

  • AM1 BCC

  • Gasteiger



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

gastrointestinal tract.



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


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