Modeling Properties with Artificial Neural Networks and Multilinear Least-Squares Regression: Advantages and Drawbacks of the Two Methods

Abstract

The mean molecular connectivity indices (MMCI) proposed in previous studies are used in conjunction with well-known molecular connectivity indices (MCI) to model eleven properties of organic solvents. The MMCI and MCI descriptors selected by the stepwise multilinear least-squares (MLS) procedure were used to perform artificial neural network (ANN) computations, with the aim of detecting the advantages and limits of the ANN approach. The MLS procedure can replicate the obtained results for as long as is needed, a characteristic not shared by the ANN methodology, which, on the one hand increases the quality of a description, and on the other hand also results in overfitting. The present study also reveals how ANN methods prefer MCI relatively to MMCI descriptors. Four types of ANN computations show that: (i) MMCI descriptors are preferred with properties with a small number of points, (ii) MLS is preferred over ANN when the number of ANN weights is similar to the number of regression coefficients and, (iii) in some cases, the MLS modeling quality is similar to the modeling quality of ANN computations. Both the common training set and an external randomly chosen validation set were used throughout the paper.

Keywords

• physicochemical properties
• QSPR
• topological descriptors
• MLS
• artificial neural networks