Henrik Boström | PubMed Health

PubMed2006

Discrimination between modes of toxic action of phenols using rule based methods.

Rule-based ensemble modelling has been used to develop a model with high accuracy and predictive capabilities for distinguishing between four different modes of toxic action for a set of 220 phenols. The model not only predicts the majority class (polar narcotics) well but also the other three classes (weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles) of toxic action despite the severely skewed distribution among the four investigated classes. Furthermore, the investigation also highlights the merits of using ensemble (or consensus) modelling as an alternative to the more traditional development of a single model in order to promote robustness and accuracy with respect to the predictive capability for the derived model.

Databases, Factual↗

PubMed2003

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.

Acetylcholinesterase↗