Molegro Virtual Docker v4.2.0 | 13.9Mb

Molegro Virtual Docker is ~y integrated platform for predicting protein - ligand interactions. Molegro Virtual Docker handles aggregate aspects of the docking process from preparation of the molecules to definition of the potential binding sites of the target protein, and foreboding of the binding modes of the ligands. Molegro Virtual Docker offers tyrannical-quality docking based on a novel optimization technique combined with a user interface actual feeling focusing on usability and productivity. The Molegro Virtual Docker (MVD) has been shown to yield higher docking precision than other state-of-the-art docking products (MVD: 87%, Glide: 82%, Surflex: 75%, FlexX: 58%).

Molegro Data Modeller offers not the same types of data modelling:
Multiple Linear Regression models simple linear relations betwixt data, and is fast and efficient.
Partial Least Squares reduces the dimensionality of the data set before creating a model. Suitable for data sets with numerous company independent variables.
Neural Networks are able to model highly non-in the direction of a line relations.
Support Vector Machines are also able to model complex relations and keep to be less prone to overfitting than Neural Networks.
K-Nearest-Neighbors instead of simple classification.

Different regression types.
Feature Selection and Cross-Validation
Feature election is easy to set up in the regression wizard: different schemes be possible to be chosen (Forward, Backward, and Hill Climber selection) and be combined with different model selection criteria (Bayes Information Criterion or cross validated R^2). Different descriptor rankings have power to be employed when searching the descriptors.

Cross-validation is just to the degree that easy. Cross-validate using a specified number of random folds, ~ means of using Leave-One-Out, or by manually creating folds.

Visualization
The divergent visualization types are highly interactive. Selections in the spreadsheet are without circumlocution shown in the plots and vice versa. It is also potential to apply different user-defined coloring schemes and apply jitter (reckon up artificial noise to the data plots).

It is possible to visualize elevated-dimensional data. Using the built-in Spring-mass Map model, to multuous-dimensional data can be projected onto 2D or 3D

Chemistry
Molegro Data Modeller supports chemical facts: MDM understands SMILES and SDF files and can create 2D depictions of molecules instantly in the spreadsheet or in the 2D plotter.

Clustering
Molegro Data Modeller offers various kinds of clustering: K-means clustering and threshold-based clustering (the couple very efficient), and a density-based clustering scheme (which is efficient to capture more complex cluster shapes).

Principal Component Analysis (PCA).
Principal Component Analysis is a process for reducing the dimensionality of a dataset. A new set of cardinal components is created using linear combinations of the original descriptors. The figure of descriptors is then reduced by only keeping the descriptors contributing ut~ to the variance.

Algebraic Data Transformations.

It is possible to be in action with algebraic transformations directly on columns: for instance, “New Activity = log(Act) + Beta^2″ power of choosing create a new column based on the expression.

Outlier Detection
Molegro Data Modeller provides sum of ~ units methods for locating abnormal data:
A quartile based method which checks in what manner far away a data point is from the 25th and 75th percentile. This method examines either descriptor individually.
A density-based method which calculates a local closeness for each data point. Data points with a low density are to a great distance away from other data points and could be outliers.

Advanced Subset Creation
Molegro Data Modeller offers a grid-based mode for creating a diverse subset of a dataset. It is practicable to create grids in an arbitrary number of dimensions, and whether or not working with 2D and 3D grids they can be visualized promptly in the data plotters.

Other Features
Scrambling (shuffling) of columns and “succeed with random values” for performing y-Randomization.
Data preparation: scaling, normalization, repair of wanting values.
Statistical measures: Pearson and Spearman correlation, Confusion matrices, F-measures, and crowd others.
Correlation Matrix.
Cross-term generation.
Custom Data Views and Grid Molecule Depictions.
Similarity Browser (Euclidean, Manhattan, Cosine, and Tanimoto measures).
Gnuplot send out (for creating and customizing publishing quality plots).
Online help and automatic check for updates.

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