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

Autor:	Jia X; The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States., Ciallella HL; The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States., Russo DP; The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States., Zhao L; The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States., James MH; Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University and Rutgers Biomedical Health Sciences, Piscataway, New Jersey 08854, United States; Brain Health Institute, Rutgers University and Rutgers Biomedical and Health Sciences, Piscataway, New Jersey 08854, United States., Zhu H; The Rutgers Center for Computational and Integrative Biology, Joint Health Sciences Center, Camden, New Jersey 08103, United States; Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States.
Jazyk:	angličtina
Zdroj:	ACS sustainable chemistry & engineering [ACS Sustain Chem Eng] 2021 Mar 15; Vol. 9 (10), pp. 3909-3919. Date of Electronic Publication: 2021 Mar 04.
DOI:	10.1021/acssuschemeng.0c09139
Abstrakt:	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 ( R 2 ) 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). Competing Interests: The authors declare no competing financial interest.
Databáze:	MEDLINE
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