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Compound Structure‐Independent Activity Prediction in High‐Dimensional Target Space
Author(s) -
Balfer Jenny,
Hu Ye,
Bajorath Jürgen
Publication year - 2014
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400051
Subject(s) - support vector machine , computer science , chemical space , artificial intelligence , machine learning , molecular descriptor , data mining , profiling (computer programming) , feature vector , naive bayes classifier , quantitative structure–activity relationship , pattern recognition (psychology) , drug discovery , bioinformatics , biology , operating system
Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi‐target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high‐dimensional target space. Different types of models were derived to predict multi‐target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high‐dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure‐based NB and SVM models or hybrid models. An in‐depth analysis of activity profile‐based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets.

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