Molegro
Virtual Docker v4.0 | 23.03 Mb
Molegro Virtual Docker is an integrated platform for predicting protein
- ligand interactions. Molegro Virtual Docker handles all aspects of
the docking process from preparation of the molecules to determination
of the potential binding sites of the target protein, and prediction of
the binding modes of the ligands. Molegro Virtual Docker offers
high-quality docking based on a novel optimization technique combined
with a user interface experience focusing on usability and
productivity. The Molegro Virtual Docker (MVD) has been shown to yield
higher docking accuracy than other state-of-the-art docking products
(MVD: 87%, Glide: 82%, Surflex: 75%, FlexX: 58%).
Molegro Data
Modeller offers different types of data modelling: Multiple
Linear Regression models simple linear relations between 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 many independent
variables. Neural Networks are able to model highly non-linear
relations. Support Vector Machines are also able to model complex
relations and tend to be less prone to overfitting than Neural Networks. K-Nearest-Neighbors
for simple classification.
Different regression types.
Feature
Selection and Cross-Validation Feature
selection is easy to set up in the regression wizard: different schemes
can 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 can be
employed when searching the descriptors.
Cross-validation is
just as easy. Cross-validate using a specified number of random folds,
by using Leave-One-Out, or by manually creating folds. Visualization The
different
visualization types are highly interactive. Selections in the
spreadsheet are directly shown in the plots and vice versa. It is also
possible to apply different user-defined coloring schemes and apply
jitter (add artificial noise to the data plots).
It is possible
to visualize high-dimensional data. Using the built-in Spring-mass Map
model, high-dimensional data can be projected onto 2D or 3D
Chemistry Molegro
Data
Modeller supports chemical data: MDM understands SMILES and SDF
files and can create 2D depictions of molecules directly in the
spreadsheet or in the 2D plotter.
Clustering Molegro
Data Modeller offers different kinds of clustering: K-means clustering
and threshold-based clustering (both very efficient), and a
density-based clustering scheme (which is able to capture more complex
cluster shapes).
Principal Component Analysis (PCA). Principal
Component
Analysis is a method for reducing the dimensionality of a
dataset. A new set of principal components is created using linear
combinations of the original descriptors. The number of descriptors is
then reduced by only keeping the descriptors contributing most to the
variance.
Algebraic Data Transformations.
It is possible
to work with algebraic transformations directly on columns: for
instance, "New Activity = log(Act) + Beta^2" will create a new column
based on the expression.
Outlier Detection Molegro Data
Modeller provides two methods for locating abnormal data: A
quartile based method which checks how far away a data point is from
the 25th and 75th percentile. This method examines each descriptor
individually. A density-based method which calculates a local
density for each data point. Data points with a low density are far
away from other data points and could be outliers.
Advanced
Subset Creation Molegro
Data Modeller offers a grid-based method for creating a diverse subset
of a dataset. It is possible to create grids in an arbitrary number of
dimensions, and if working with 2D and 3D grids they can be visualized
directly in the data plotters.
Cross-Platform Molegro
Data Modeller works with: Windows XP and Vista. Mac OS X (10.4 and
later, PowerPC and Intel supported). Most major Linux distributions.
Other
Features Scrambling (shuffling) of columns and "replace with
random values" for performing y-Randomization. Data preparation:
scaling, normalization, repair of missing values. Statistical
measures: Pearson and Spearman correlation, Confusion matrices,
F-measures, and many others. Correlation Matrix. Cross-term
generation. Custom Data Views and Grid Molecule Depictions. Similarity
Browser (Euclidean, Manhattan, Cosine, and Tanimoto measures). Gnuplot
export (for creating and customizing publishing quality plots). Online
help and automatic check for updates.
Home Page -
http://www.molegro.com/mmv-product.php
