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Classifying “Kinase Inhibitor‐Likeness” by Using Machine‐Learning Methods
Author(s) -
Briem Hans,
Günther Judith
Publication year - 2005
Publication title -
chembiochem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.05
H-Index - 126
eISSN - 1439-7633
pISSN - 1439-4227
DOI - 10.1002/cbic.200400109
Subject(s) - support vector machine , artificial intelligence , computer science , artificial neural network , machine learning , set (abstract data type) , pattern recognition (psychology) , feature selection , value (mathematics) , precision and recall , feature (linguistics) , k nearest neighbors algorithm , data mining , linguistics , philosophy , programming language
By using an in‐house data set of small‐molecule structures, encoded by Ghose–Crippen parameters, several machine learning techniques were applied to distinguish between kinase inhibitors and other molecules with no reported activity on any protein kinase. All four approaches pursued—support‐vector machines (SVM), artificial neural networks (ANN), k nearest neighbor classification with GA‐optimized feature selection (GA/ k NN), and recursive partitioning (RP)—proved capable of providing a reasonable discrimination. Nevertheless, substantial differences in performance among the methods were observed. For all techniques tested, the use of a consensus vote of the 13 different models derived improved the quality of the predictions in terms of accuracy, precision, recall, and F1 value. Support‐vector machines, followed by the GA/ k NN combination, outperformed the other techniques when comparing the average of individual models. By using the respective majority votes, the prediction of neural networks yielded the highest F1 value, followed by SVMs.