Analysis of Activity Space by Fragment Fingerprints, 2D Descriptors, and Multitarget Dependent Transformation of 2D Descriptors

Abstract

The effect of multitarget dependent descriptor transformation on classification performance is explored in this work. To this end decision trees as well as neural net QSAR in combination with PLS were applied to predict the activity class of 5HT3 ligands, angiotensin converting enzyme inhibitors, 3-hydroxyl-3-methyl glutaryl coenzyme A reductase inhibitors, platelet activating factor antagonists, and thromboxane A2 antagonists. Physicochemical descriptors calculated by MOE and fragment-based descriptors (MOLPRINT 2D) were employed to generate descriptor vectors. In a subsequent step the physicochemical descriptor vectors were transformed to a lower dimensional space using multitarget dependent descriptor transformation. Cross-validation of the original physicochemical descriptors in combination with decision trees and neural net QSAR as well as cross-validation of PLS multitarget transformed descriptors with neural net QSAR were performed. For comparison this was repeated using fragment-based descriptors in combination with decision trees.