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A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems
 

Summary: A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant
Colony Systems
Sergei Izrailev* and Dimitris Agrafiotis
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341
Received July 25, 2000
Among the multitude of learning algorithms that can be employed for deriving quantitative structure-
activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically
perform the key feature selection, and yield readily interpretable models. A conventional method of building
a regression tree model is recursive partitioning, a fast greedy algorithm that works well in many, but not
all, cases. This work introduces a novel method of data partitioning based on artificial ants. This method is
shown to perform better than recursive partitioning on three well-studied data sets.
I. INTRODUCTION
The use of artificial intelligence algorithms, such as k
nearest neighbors, classification and regression trees, and
neural networks, for structure-activity correlation has vastly
increased over the past few years, due to the growing
availability of biological data and the rising demand for more
accurate and interpretable models for pharmaceutical devel-
opment. Regression trees1 offer several advantages over
alternative quantitative structure-activity relationship (QSAR)

  

Source: Agrafiotis, Dimitris K. - Molecular Design and Informatics Group, Johnson & Johnson Pharmaceutical Research and Development

 

Collections: Chemistry; Computer Technologies and Information Sciences