| Literature DB >> 32275393 |
Nicole Pribut1, Thomas M Kaiser1, Robert J Wilson1, Edgars Jecs1, Zackery W Dentmon1, Stephen C Pelly1, Savita Sharma1, Perry W Bartsch1, Pieter B Burger1, Soyon S Hwang1, Thalia Le1, Julien Sourimant2, Jeong-Joong Yoon2, Richard K Plemper2, Dennis C Liotta1.
Abstract
A series of five benzimidazole-based compounds were identified using a machine learning algorithm as potential inhibitors of the respiratory syncytial virus (RSV) fusion protein. These compounds were synthesized, and compound 2 in particular exhibited excellent in vitro potency with an EC50 value of 5 nM. This new scaffold was then further refined leading to the identification of compound 44, which exhibited a 10-fold improvement in activity with an EC50 value of 0.5 nM.Entities:
Keywords: RSV; benzimidazole; fusion inhibitor; machine learning
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Year: 2020 PMID: 32275393 PMCID: PMC7456560 DOI: 10.1021/acsinfecdis.9b00524
Source DB: PubMed Journal: ACS Infect Dis ISSN: 2373-8227 Impact factor: 5.084