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In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance

机译:计算机方法筛选对主要社会经济意义上的寄生性线虫有活性的化合物

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摘要

BackgroundInfections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes.
机译:背景技术寄生线虫引起的感染是世界范围内发病率和致死率的常见原因,尤其是在发展中国家。但是,目前只有三种主要的用于治疗人线虫感染的药物。另外,关于作用机理和对这些药物产生抗药性的原因的科学知识知之甚少。设计在发展中国家流行的药物的商业动机是有限的,因此,在学术环境中进行虚拟筛选可以发挥至关重要的作用,这是发现对付被忽视疾病有用的新药。在这项研究中,我们建议建立健壮的机器学习模型来分类和筛选对寄生性线虫有活性的化合物。

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