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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

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Author(s):
Gawriljuk, Victor O. [1] ; Foil, Daniel H. [2] ; Puhl, Ana C. [2] ; Zorn, Kimberley M. [2] ; Lane, Thomas R. [2] ; Riabova, Olga [3] ; Makarov, Vadim [3] ; Godoy, Andre S. [1] ; Oliva, Glaucius [1] ; Ekins, Sean [2]
Total Authors: 10
Affiliation:
[1] Univ Sao Paulo, Sao Carlos Inst Phys, BR-13563120 Sao Carlos, SP - Brazil
[2] Collaborat Pharmaceut Inc, Raleigh, NC 27606 - USA
[3] Res Ctr Biotechnol RAS, Moscow 119071 - Russia
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 61, n. 8, p. 3804-3813, AUG 23 2021.
Web of Science Citations: 0
Abstract

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 mu M and CC50 24 mu M, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF. (AU)

FAPESP's process: 13/07600-3 - CIBFar - Center for Innovation in Biodiversity and Drug Discovery
Grantee:Glaucius Oliva
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 19/25407-2 - Development of machine learning models for the discovery of new antiviral compounds against yellow fever virus
Grantee:Victor Gawriljuk Ferraro Oliveira
Support type: Scholarships abroad - Research Internship - Master's degree