Application of machine learning approaches on quantitative structure activity relationships

Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as active or inactive with respect to a specific target biological system. This paper presents a comparison of artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) in an effort to identify potentiators of metabotropic glutamate receptor 5 (mGluR5), compounds that have potential as novel treatments against schizophrenia. When training and testing each of the three techniques on the same dataset enrichments of 61, 64, and 43 were obtained and an area under the curve (AUC) of 0.77, 0.78, and 0.63 was determined for ANNs, SVMs, and DTs, respectively. For the top percentile of predicted active compounds, the true positives for all three methods were highly similar, while the inactives were diverse offering the potential use of jury approaches to improve prediction accuracy.