Development of Neural Network QSPR Models for Hansch Substituent Constants. 1. Method and Validations
Ting‐Lan Chiu, Sung‐Sau So · Journal of Chemical Information and Computer Sciences · 2003
In an attempt to develop predictive models for Hansch substituent constants for less common substituents, neural network QSPR (Quantitative Structure-Property Relationship) studies were conducted to correlate Hansch substituent constants for hundreds of chemically diverse functional groups with two different molecular descriptor sets. The Hansch substituent constants under study were pi, MR, F and R, describing the hydrophobic, steric/polarizability, and electronic (field and resonance) characteristics of the substituents, respectively. E-state descriptors were used for pi and MR, while the molecular descriptor set based upon the approach of Kvasnicka, Sklenak, and Pospichal (J. Am. Chem. Soc. 1993, 115, 1495-1500) was adopted for F and R. Both QSPR models demonstrated good predictivity in test sets.