BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs

Das, Pranab Jyoti; Thakran, Yogita; Anal, S R Ngamwal; Pal, Vipin; Yadav, Anju

doi:10.1109/tcbb.2022.3215645

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…Chemical feature of medicine plays an essential role in drug development [35]. The three‐dimensional chemical conformer of medicine is a vital feature of medicine [36].…”

Section: Proposed Methodology and Workflow Of The Frameworkmentioning

confidence: 99%

MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer

Das

Mazumder

2023

ETRI Journal

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To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer‐learning methodology and a multilabel convolutional neural network for COVID (MLCNN‐COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer‐learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN‐COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three‐dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN‐COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer‐learning model's performance. It shows that three‐dimensional conformers adequately identify negative COVID medicine responses.

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“…Chemical feature of medicine plays an essential role in drug development [35]. The three‐dimensional chemical conformer of medicine is a vital feature of medicine [36].…”

Section: Proposed Methodology and Workflow Of The Frameworkmentioning

confidence: 99%