Prediction of antischistosomal small molecules using machine learning in the era of big data
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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 diferent 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 frst discusses the chal lenges of drug discovery in schistosomiasis. Explanations are then ofered 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 artifcial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis.
The use of binary classifcation 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 classifcation. Databases
specifcally designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets
and ontologies are thus urgently needed for developing predictive ML models.
Description
Research Article
Keywords
Machine learning, Deep learning, Artifcial intelligence, Big data, Drug discovery, Schistosomiasis, Classifers, Binary classifcation