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

ABSTRACT: 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 coef

SUBMITTER: Jia X 

PROVIDER: S-EPMC8259887 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature