A shape-based machine learning tool for drug design.
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.
Jain
A N
AN
Arris Pharmaceutical Corporation, South San Francisco, CA 94080, USA.
Dietterich
T G
TG
Lathrop
R H
RH
Chapman
D
D
Critchlow
R E
RE
Bauer
B E
BE
Webster
T A
TA
Lozano-Perez
T
T
D000465:Algorithms; D017076:Computer-Aided Design; D015195:Drug Design; D005229:Fatty Acids, Monounsaturated / Q000737:chemistry; D006801:Humans; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis; D012984:Software
D005229:Fatty Acids, Monounsaturated; C008563:musk