Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed
to correlate chemical molecules with their biological activities based on their chemical structures.
Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to
the significant role of machine learning strategies in QSAR modeling, this area of research has attracted
much attention from researchers. A considerable amount of literature has been published on
machine learning based QSAR modeling methodologies whilst this domain still suffers from lack
of a recent and comprehensive analysis of these algorithms. This study systematically reviews the
application of machine learning algorithms in QSAR, aiming to provide an analytical framework.
For this purpose, we present a framework called ‘ML-QSAR‘. This framework has been designed
for future research to: a) facilitate the selection of proper strategies among existing algorithms according
to the application area requirements, b) help to develop and ameliorate current methods
and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a
structured categorization is depicted which studied the QSAR modeling research based on machine
models. Then several criteria are introduced in order to assess the models. Finally, inspired by
aforementioned criteria the qualitative analysis is carried out.
Keywords: QSAR modeling, machine learning, drug discovery, drug design, computational intelligence, drug design, ADME/T modeling.
Rights & PermissionsPrintExport