ICM Manual v.3.8
by Ruben Abagyan,Eugene Raush and Max Totrov
Copyright © 2018, Molsoft LLC
Aug 16 2018

Contents
 
Introduction
Reference Guide
 ICM options
 Editing
 Graph.Controls
 Alignment Editor
 Constants
 Subsets
 Molecules
 Selections
 Fingerprints
 Regexp
 Cgi programming with icm
 Xml drugbank example
 Tree cluster
 Arithmetics
 Flow control
 MolObjects
 Energy Terms
 Integers
 Reals
 Logicals
 Strings
 Preferences
 Tables
 Other
 Chemical
 Smiles
 Soap
 Gui programming
 Commands
 Functions
 Icm shell functions
 Macros
 Files
Command Line User's Guide
References
Glossary
 
Index
PrevICM Language Reference
Chemical Fingerprints
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We construct fingerprint by enumerating linear and some non-linear fragments plus some information about rings. Atoms type mapping varies depending on the chain/fragment size. All this hashed into counted or non-counted vector. (counted are mostly used in prediction models)

Fingerprint properties:

  • Result vector size : (1536 for default chemical similarity/distance fingerprint)
  • Minimum/Maximum fragment size (chain length for linear fingerprints) : 1-6 default
  • Flexible atom typing which from the combination of the following features:
    • cd : element code
    • hyb: hybridization
    • a : aromaticity.
    • h : number of connected hydrogen
    • hp : number of connected hydrogen for polar atoms (0- others)
    • r : ring/chain attribute
    • b : number of heavy neighbors
    • rs : size of the smallest ring atom belongs to (0 - for non-ring atoms)
  • Flexible bond typing:
    • bt : bond order
    • ~r : ring/chain attribute
    • ~rt : rotatable attribute
  • enumeration method:
    • linear chains, rings and small branched fragments (this is the default for chemical distance/similarity)
    • ecfp (non-linear fragment enumeration)
    • 2D ph4
  • binary / counted

For the chemical distance/similarity and clustering the default fingerprint type type is 1. Length: 1536 Max linear chain length: 6 Atom typing depends on the chain length.

Custom fingerprints can be built using Descriptor function or in prediction models


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