Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

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Abstract

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

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Most cited references 384

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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NIH Image to ImageJ: 25 years of image analysis

Kevin W Eliceiri, Caroline A Schneider, Wayne S Rasband (2019)

For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

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Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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Author and article information

Contributors

José T. Moreira-Filho: URI : https://loop.frontiersin.org/people/633274

Arthur C. Silva: URI : https://loop.frontiersin.org/people/843903

Rafael F. Dantas: URI : https://loop.frontiersin.org/people/1229658

Barbara F. Gomes: URI : https://loop.frontiersin.org/people/1189610

Lauro R. Souza Neto: URI : https://loop.frontiersin.org/people/861419

Raymond J. Owens: URI : https://loop.frontiersin.org/people/1121230

Nicholas Furnham: URI : https://loop.frontiersin.org/people/205816

Bruno J. Neves: URI : https://loop.frontiersin.org/people/1304782

Floriano P. Silva-Junior: URI : https://loop.frontiersin.org/people/585187

Carolina H. Andrade: URI : https://loop.frontiersin.org/people/447298

Journal

Journal ID (nlm-ta): Front Immunol

Journal ID (iso-abbrev): Front Immunol

Journal ID (publisher-id): Front. Immunol.

Title: Frontiers in Immunology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-3224

Publication date (Electronic): 31 May 2021

Publication date Collection: 2021

Volume: 12

Electronic Location Identifier: 642383

Affiliations

[1] ¹ LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG , Goiânia, Brazil

[2] ² LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz , Rio de Janeiro, Brazil

[3] ³ Diamond Light Source Ltd. , Didcot, United Kingdom

[4] ⁴ Research Complex at Harwell , Didcot, United Kingdom

[5] ⁵ The Rosalind Franklin Institute , Harwell, United Kingdom

[6] ⁶ Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford , Oxford, United Kingdom

[7] ⁷ Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine , London, United Kingdom

Author notes

Edited by: Thiago Almeida Pereira, Stanford University, United States

Reviewed by: Conor R. Caffrey, University of California, San Diego, United States; Lizandra Guidi Magalhães, University of Franca, Brazil; Douglas Pires, The University of Melbourne, Australia

*Correspondence: Carolina H. Andrade, carolina@ 123456ufg.br

This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology

Article

DOI: 10.3389/fimmu.2021.642383

PMC ID: 8203334

PubMed ID: 34135888

SO-VID: 1a6444a6-e8c6-4aaf-8bac-c2df3aa1b963

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 15 December 2020

Date accepted : 30 April 2021

Page count

Figures: 4, Tables: 4, Equations: 0, References: 388, Pages: 29, Words: 14914

Funding

Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico 10.13039/501100003593

Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 10.13039/501100002322

Funded by: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro 10.13039/501100004586

Funded by: Fundação de Amparo à Pesquisa do Estado de Goiás 10.13039/501100005285

Funded by: Fundação Oswaldo Cruz 10.13039/501100006507

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Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

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Abstract

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Chinese Journal of Schistosomiasis Control

Most cited references 384

Gene Ontology: tool for the unification of biology

NIH Image to ImageJ: 25 years of image analysis

Deep learning.

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Cited by 7

Most referenced authors 7,377