Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids

Compared
to traditional experimental approaches, computational
modeling is a promising strategy to efficiently prioritize new candidates
with low cost. In this study, we developed a novel data mining and
computational modeling workflow proven to be applicable by screening
new analgesic opioids. To this end, a large opioid data set was used
as the probe to automatically obtain bioassay data from the PubChem
portal. There were 114 PubChem bioassays selected to build quantitative
structure–activity relationship (QSAR) models based on the
testing results across the probe compounds. The compounds tested in
each bioassay were used to develop 12 models using the combination
of three machine learning approaches and four types of chemical descriptors.
The model performance was evaluated by the coefficient of determination
(R2) obtained from 5-fold cross-validation.
In total, 49 models developed for 14 bioassays were selected based
on the criteria and were identified to be mainly associated with binding
affinities to different opioid receptors. The models for these 14
bioassays were further used to fill data gaps in the probe opioids
data set and to predict general drug compounds in the DrugBank data
set. This study provides a universal modeling strategy that can take
advantage of large public data sets for computer-aided drug design
(CADD).