Limitations of Functional‐Link Nets as Applied to QSAR Data Analysis
David T. Manallack, David J. Livingstone · Quantitative Structure-Activity Relationships · 1994
Abstract This report compares the results of QSAR analyses examining a series of bis(1‐aziridinyl)‐p‐benzoquinones using multiple linear regression (MLR), neural networks and a network hybrid technique known as a functional‐link net (FUNCLINK). The FUNCLINK approach generates an expanded list of new parameters and seemed to outperform MLR as well as providing an apparently superior predictive ability. One observation from this work showed that neural networks generated higher correlation coefficients than MLR. This phenomenon, however, may be due to the inability of neural networks to predict values beyond their operating range. On examination of the results it appears that there are two disadvantages to the FUNCLINK technique. Firstly, the natural ability of neural networks to develop non‐linear relationships is removed with FUNCLINK as these must be specified. Secondly, the large number of enhanced parameters produced by FUNCLINK must increase the possibility of chance effects. From the results presented here it would appear that FUN‐CLINK adds little to the field of QSAR data analysis.