A shape-based machine learning tool for drug design.
J Comput Aided Mol Des
; 8(6): 635-52, 1994 Dec.
Article
in En
| MEDLINE
| ID: mdl-7738601
ABSTRACT
Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
Drug Design
/
Computer-Aided Design
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Comput Aided Mol Des
Journal subject:
BIOLOGIA MOLECULAR
/
ENGENHARIA BIOMEDICA
Year:
1994
Document type:
Article
Affiliation country:
United States