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.

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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.

Keywords: automated QSARkernel PLSpredictionQSARvalidation

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 blockFut Med Chem. 10(3) 335-342, (2018) 

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Future Medicinal Chemistry

Journal, Future Science Group

Future Medicinal Chemistry provides a monthly point of access to commentary and debate for this ever-expanding and diversifying community. The journal showcases milestones in pharmaceutical R&D and features expert analysis of emerging research – from the identification of targets, through to the discovery, design, synthesis and evaluation of bioactive agents.

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