  Quantitative Structure-Activity Relationship (QSAR) has proved 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.
