Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use.

Abstract

Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundation for data-driven modelling approaches that have fostered the development of machine learning and artificial intelligence tools. An overview and discussion of the principles of QSAR modelling focus on the assembly and curation of data, computation of molecular descriptor, optimization, validation, and definition of the scope of the developed QSAR models. In this review, some examples of (Q)SAR models based on artificial neural networks are given to demonstrate the effectiveness of nonlinear methods for extracting information from large data sets to classify new chemicals and predict their biological properties.