The Use of Artificial Neural Networks in QSAR

TLDR

Artificial neural networks, inspired by brain information processing, have been applied across diverse domains including image analysis, traffic management, handwriting verification, stock market prediction, and increasingly in computer‑aided molecular design and QSAR studies. The study investigates the properties of ANN when applied to multivariate statistical analyses in QSAR. The authors focus on the multi‑layer feed‑forward back‑propagation perceptron (MLP), compare it with multiple linear regression, and conduct experiments with artificial data to illustrate MLP operation. They present two MLP applications: a QSAR analysis and secondary protein structure prediction in computer‑aided design.

Abstract

Abstract Artificial neural networks (ANN) have their origins in efforts to produce computer models of the information processing that takes place in the brain. They have found application in a wide variety of fields such as image analysis of facial features, traffic management of underground station platforms, hand‐writing verification of cheques, stock market predictions, etc. They have also been applied to computer‐aided molecular design, notably protein structure prediction, and more recently ANN have been used to perform statistical tasks such as discriminant analysis and multiple linear regression in the investigation of Quantitative Structure‐Activity Relationships (QSAR). We have begun a study of the properties of ANN when used to perform such multivariate statistical analyses. The most popular network used in QSAR‐type applications is the multi‐layer feed‐forward network, also known as the back propagation multi‐layer perceptron (MLP). The approaches of MLP and multiple linear regression to modelling are discussed. In order to give some insight into the operation of MLP networks we have carried out experiments with artificial data. Finally, we report two examples of MLP in computer‐aided design, a QSAR analysis and the prediction of secondary protein structure.