Improvised CNN model to Predict SARS by Detecting the Localisation of Proteins

Devi, M. Nirmala; Mahima, S.; Ramupriya, R.; Sathar, Sumaya Abdul

doi:10.1109/iceca52323.2021.9676045

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…As a greater number of drugs become ineffective against the bacteria, the prevalence of resistant bacteria is becoming a growing concern for both the general public and the pharmaceutical business. Despite the fact that antibiotic therapy is in line with modern medicine, a decline in funding makes it difficult for investigators to stay informed of the actual population's healthcare needs Aslam et al (2018) [1]. Traditional drug discovery takes a long and is exorbitant; for example, in 2006, the Food and Drug Administration (FDA) only approved 22 potential biological entities in spite of enormous research and development costs of up to $93 billion USD Yu et al (2012) [2].…”

Section: Introductionmentioning

confidence: 99%

Drug Target Interaction [DTI] and Prediction using Machine Learning

M¹,

Rakesh²

2022

IJRASET

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The need to find new antibiotics is expanding as a result of the quick rise in bacteria that are resistant to medicines. Discovering drug-protein interactions could be an essential first step in the process of developing drugs since it will substantially reduce the scope of the look for possible solutions. Since in vitro assays are extremely time-consuming and pricey. We developed a machine learning method that can predict medications for the target in order to overcome this difficulty. We used the Padel script to do predictions on several chemical libraries, acquire drug physical and chemical properties, and obtain features extracted. establishing which model is best for predicting drug-target interactions is performed by analyzing the Random Forest technique with the Naive Bayes method, K-Nearest Neighbor, and other choices. This study reduces the failure rates and costs incurred when creating new pharmaceuticals while demonstrating the value of adopting machine learning approaches in drug discovery.

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Section: Introductionmentioning

confidence: 99%