Contemporary QSAR Classifiers Compared

• Published in: Journal of chemical information and modeling Vol. 47; no. 1; pp. 219 - 227
• Main Authors: Bruce, Craig L, Melville, James L, Pickett, Stephen D, Hirst, Jonathan D
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.01.2007
• Subjects: Algorithms; Artificial Intelligence; Classification; Comparative analysis; Drugs; Models, Statistical; Quantitative Structure-Activity Relationship
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci600332j

We present a comparative assessment of several state-of-the-art machine learning tools for mining drug data, including support vector machines (SVMs) and the ensemble decision tree methods boosting, bagging, and random forest, using eight data sets and two sets of descriptors. We demonstrate, by rigorous multiple comparison statistical tests, that these techniques can provide consistent improvements in predictive performance over single decision trees. However, within these methods, there is no clearly best-performing algorithm. This motivates a more in-depth investigation into the properties of random forests. We identify a set of parameters for the random forest that provide optimal performance across all the studied data sets. Additionally, the tree ensemble structure of the forest may provide an interpretable model, a considerable advantage over SVMs. We test this possibility and compare it with standard decision tree models.