On the virtues of automated quantitative structure–activity relationship: the new kid on the block
In this special report published recently in Future Medicinal Chemistry, Marcelo de Oliveira and Edson Katekawa (both Universidade de São Paulo, Brazil) discuss the value of using automated QSAR approaches.
Quantitative structure–activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.
Read the full report here.
de Oliveira MT and Katekawa E. On the virtues of automated quantitative structure-activity relationship: the new kid on the block. Fut Med Chem. 10(3) 335-342, (2018)