Genetic Neural Networks for Functional Approximation

Abstract Quantitative structure‐activity relationship (QSAR) analysis is a commonly used ligand‐based molecular design method for the lead optimization process in the pharmaceutical industry. Typically, development of a QSAR model goes through the following stages: descriptor generation, function approximation (including feature selection and model construction), and model validation. This article highlights a promising genetic neural network (GNN) method for function approximation in QSAR and reviews its underlying theoretical concepts, techniques, and recent applications.