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

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Prediction of antischistosomal small molecules using machine learning in the era of big data
Molecular Diversity ( IF 3.9 ) Pub Date : 2021-08-05 , DOI: 10.1007/s11030-021-10288-2
Samuel K Kwofie _{1,

2} , Kwasi Agyenkwa-Mawuli _{1,

2} , Emmanuel Broni _{1,

3} , Whelton A Miller Iii _{4,

5,

6} , Michael D Wilson _{3,

4}

Affiliation

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.

Graphic abstract

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.

中文翻译：

大数据时代使用机器学习预测抗血吸虫小分子

血吸虫病是一种被忽视的热带疾病，由血吸虫属的蠕虫引起。尽管其高发病率和社会经济负担，但治疗药物只是少数，吡喹酮是主要药物。吡喹酮是一种于 1982 年注册供人使用的老药，此后大量用于化疗，有产生耐药性的风险，因此需要具有不同作用机制的新药。这篇综述探讨了机器学习 (ML) 在这个大数据时代的应用，以帮助预测新的抗血吸虫分子。它首先讨论了血吸虫病药物发现的挑战。然后对大数据及其特征进行解释，然后检查一些开放数据库，在这些数据库中可以获得大量的血吸虫病生化数据，用于 ML 模型开发。在血吸虫病中探索了人工智能、机器学习和深度学习的概念及其药物应用。讨论了二元分类在预测抗血吸虫化合物中的应用以及一些已应用的算法，包括随机森林和朴素贝叶斯。在这篇综述中，提出了一些深度学习算法（深度神经网络）作为通过二元分类预测抗血吸虫分子的新算法。因此，迫切需要专门设计用于容纳富含功能基因组数据集和本体的抗血吸虫分子生物活性数据的数据库，以开发预测性 ML 模型。讨论了二元分类在预测抗血吸虫化合物中的应用以及一些已应用的算法，包括随机森林和朴素贝叶斯。在这篇综述中，提出了一些深度学习算法（深度神经网络）作为通过二元分类预测抗血吸虫分子的新算法。因此，迫切需要专门设计用于容纳富含功能基因组数据集和本体的抗血吸虫分子生物活性数据的数据库，以开发预测性 ML 模型。讨论了二元分类在预测抗血吸虫化合物中的应用以及一些已应用的算法，包括随机森林和朴素贝叶斯。在这篇综述中，提出了一些深度学习算法（深度神经网络）作为通过二元分类预测抗血吸虫分子的新算法。因此，迫切需要专门设计用于容纳富含功能基因组数据集和本体的抗血吸虫分子生物活性数据的数据库，以开发预测性 ML 模型。