Affiliation:
1. Theory Department, National Institute of Chemistry, Ljubljana, Slovenia
Abstract
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.
Funder
Slovenian Research Agency ARRS
Marie Skłodowska- Curie Action - Innovative Training Network
Publisher
Bentham Science Publishers Ltd.
Subject
Drug Discovery,General Medicine