Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity.
Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.
Papadatos
George
G
Information School, University of Sheffield, Sheffield S1 4DP, UK.
Alkarouri
Muhammad
M
Gillet
Valerie J
VJ
Willett
Peter
P
Kadirkamanathan
Visakan
V
Luscombe
Christopher N
CN
Bravi
Gianpaolo
G
Richmond
Nicola J
NJ
Pickett
Stephen D
SD
Hussain
Jameed
J
Pritchard
John M
JM
Cooper
Anthony W J
AW
Macdonald
Simon J F
SJ
D000465:Algorithms; D016208:Databases, Factual; D015195:Drug Design; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008024:Ligands; D008055:Lipids / Q000737:chemistry; D015394:Molecular Structure; D012995:Solubility
D051638:Ether-A-Go-Go Potassium Channels; D008024:Ligands; D008055:Lipids