Automated Protocol for Large-Scale Modeling of Gene Expression Data

With the continued
rise of phenotypic- and genotypic-based screening
projects, computational methods to analyze, process, and ultimately
make predictions in this field take on growing importance. Here we
show how automated machine learning workflows can produce models that
are predictive of differential gene expression as a function of a
compound structure using data from A673 cells as a proof of principle.
In particular, we present predictive models with an average accuracy
of greater than 70% across a highly diverse ∼1000 gene expression
profile. In contrast to the usual in silico design paradigm, where
one interrogates a particular target-based response, this work opens
the opportunity for virtual screening and lead optimization for desired
multitarget gene expression profiles.