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Contents
 
Introduction
Help Videos
Reference Guide
Getting Started
Protein Structure
Molecular Graphics
Slides & ActiveICM
Sequences & Alignments
Protein Modeling
Cheminformatics
Learn and Predict
 Learn
 Predict
 Fingerprint Methods
 3D QSAR
 Theory
Docking
Virtual Screening
MolScreen
3D Ligand Editor
Tables and Plots
Local Databases
ICM-Scarab
KNIME
Tutorials
FAQs
 
Index
PrevICM User's Guide
11 Learn and Predict
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Available in the following product(s): ICM-Chemist-Pro | ICM-VLS
In this chapter:

There are four learning algorithms built into ICM

All 2D molecular property predictors are calculated using fragment-based contributions and 3D models use Atomic Property Fields .

For 2D fingerprints we developed an original method for splitting a molecule into a set of linear or non-linear fragments of different length and representation levels and then each chemical pattern found is converted into a descriptor.

In order to perform 'learn and predict' in ICM information must be stored in a table, molecular table or csv file. See the tables chapter for more information on ICM tables. Both chemical compounds and numeric data can be source for building prediction models.


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