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Prediction of antischistosomal small molecules using machine learning in the era of big data

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Abstract

Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with different mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It first discusses the challenges of drug discovery in schistosomiasis. Explanations are then offered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artificial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classification in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classification. Databases specifically designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models.

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This shows the application of machine learning techniques for the discovery of novel antischistosomal small molecules via binary classification in the era of big data.

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, P.O. Box LG 77, Accra, Ghana

    Samuel K. Kwofie, Kwasi Agyenkwa-Mawuli & Emmanuel Broni

  2. West African Centre for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, University of Ghana, Legon, P.O. Box LG 54, Accra, Ghana

    Samuel K. Kwofie & Kwasi Agyenkwa-Mawuli

  3. Department of Medicine, Loyola University Medical Center, Maywood, IL, 60153, USA

    Whelton A. Miller III & Michael D. Wilson

  4. Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL, 60153, USA

    Whelton A. Miller III

  5. Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Whelton A. Miller III

  6. Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, P.O. Box LG 581, Accra, Ghana

    Emmanuel Broni & Michael D. Wilson

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SKK conceptualized the review. SKK and KAM co-wrote the first draft with contributions from WAM, EB and MDW. All authors read and accepted the final draft for submission.

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Kwofie, S.K., Agyenkwa-Mawuli, K., Broni, E. et al. Prediction of antischistosomal small molecules using machine learning in the era of big data. Mol Divers 26, 1597–1607 (2022). https://doi.org/10.1007/s11030-021-10288-2

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