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Computational modeling studies on anti-HIV-1 non-nucleoside reverse transcriptase inhibition by dihydroalkoxybenzyloxopyrimidines analogues: an electrotopological atomistic approach

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Journal of Biophysical Chemistry 2011年第3期2卷 361-372页

作者： Nitin S. Sapre Tarang Bhati swagata gupta Nilanjana Pancholi Urmila Raghuvanshi Divya Dubey Vandana Rajopadhyay Neelima Sapre Department of Applied Chemistry SGSITS Indore MP INDIA

For the first time we report quantitative structure activity relationship (QSAR) studies based on Kier-Hall Electrotopological State (E-State) Indices for Dihydroalkoxybenzyloxopyrimidines (DABO) derivatives acting as NNRTIs of HIV-1. A dataset of 74 compounds was compiled from published studies and randomly subdivided into training and test sets. To understand the pharmacophoric effect, Kier-Hall Electrotopological State descriptors namely SN1, SN3, SF, SAr, SS, SO, SNO2, SCl, SY (Y = S-alkyl and NH-alkyl), SX (X = Me) and biological activity were used as independent and dependent variable respectively. Statistical results were highly encouraging for the training set multiple linear regression [(MLR): r2 = 0.961, F = 100.41 and q2 = 0.926, neural networks (NN): r2 = 0.966, F = 115.594, degrees of freedom = 40 and k-nearest neighbour (k-NN): r2 = 0.770, q2 = 0.757, degrees of freedom = 40]. Results of validation using a test set showed the same trend as training set (NN > MLR > kNN). The above results suggest that of various functional groups present in DABO such as SN3, SO, SCl, SAr and SNO2 contribute more significantly towards activity. On the other hand SN1, SS, and SF do not play any role in enhancing the activity. The substitution of S-alkyl and NH-alkyl at C2 position is essential though it does not contribute much towards the activity. The substitution of methyl group at C5 position is unfavorable and exhibit negative impact on inhibitory activity. Therefore, it seems reasonable to choose E-state indices as suitable and significant descriptors for exploring the relationship between the pIC50 and the pharmacological properties of the compounds.

关键词： AIDS HIV-1 NNRTIs DABOs QSAR pIC50 Kier Hall E-State Indices MLR NN k-NN

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Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy Detection

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Computers, Materials & Continua 2023年第4期75卷 927-942页

作者： swagata Boruah Archit Dehloo Prajul gupta Manas Ranjan Prusty A.Balasundaram School of Electronics Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

Diabetic Retinopathy (DR) is a serious hazard that can result inirreversible blindness if not addressed in a timely manner. Hence, numeroustechniques have been proposed for the accurate and timely detection ofthis disease. Out of these, Deep Learning (DL) and Computer Vision (CV)methods for multiclass categorization of color fundus images diagnosed withDiabetic Retinopathy have sparked considerable attention. In this paper,we attempt to develop an extended ResNet152V2 architecture-based DeepLearning model, named ResNet2.0 to aid the timely detection of DR. TheAPTOS-2019 datasetwas used to train the model. This consists of 3662 fundusimages belonging to five different stages of DR: no DR (Class 0), mild DR(Class 1), moderate DR (Class 2), severe DR (Class 3), and proliferativeDR (Class 4). The model was gauged based on ability to detect stage-wiseDR. The images were pre-processed using negative and positive weightedGaussian-based masks as feature engineering to further enhance the qualityof the fundus images by removing the noise and normalizing the images. Upsamplingand data augmentation methods were used to address the skewnessof the original dataset. The proposed model achieved an overall accuracyof 91% and an area under the receiver-operating characteristic curve (AUC)score of 95.1%, outperforming existing Deep Learning models by around 10%.Furthermore, the class-wise F1 score for No DR was 92%, Mild DR was 82%,Moderate DR was 66%, Severe was DR 89% and Proliferative DR was 80%.

关键词： Diabetic retinopathy deep learning transfer learning image processing image classification

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