Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.
We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.
Novartis Institute for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. anthony.klon@pharma.novartis.com
Anthony E
AE
Klon
Meir
M
Glick
John W
JW
Davies
D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D015195:Drug Design; D015233:Models, Statistical; D011485:Protein Binding; D011506:Proteins / Q000037:antagonists & inhibitors*
D011506:Proteins