Improving structure-based virtual screening by multivariate analysis of scoring data.
Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.
Jacobsson
Micael
M
Structural Chemistry, Biovitrum AB, SE-112 76 Stockholm, Sweden. micael.jacobsson@biovitrum.com
Lidén
Per
P
Stjernschantz
Eva
E
Boström
Henrik
H
Norinder
Ulf
U
D000110:Acetylcholinesterase / Q000737:chemistry; D001665:Binding Sites; D047628:Estrogen Receptor alpha; D015951:Factor Xa / Q000737:chemistry; D008024:Ligands; D019278:Matrix Metalloproteinase 3 / Q000737:chemistry; D015999:Multivariate Analysis; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000737:chemistry
D047628:Estrogen Receptor alpha; D008024:Ligands; D011960:Receptors, Estrogen; D000110:Acetylcholinesterase; D015951:Factor Xa; D019278:Matrix Metalloproteinase 3