Molecular docking based comparative study of antiviral compounds on SARS-CoV-2 spike protein.

The urgent need for effective therapeutic interventions against SARS-CoV-2 has prompted extensive exploration of potential drug candidates. Among the viral proteins, the spike (S) protein presents an attractive target due to its critical role in viral entry and infection. In this study, we employed molecular docking techniques to investigate the binding affinities and interaction profiles of a panel of active compounds against the SARS-CoV-2 spike protein. Utilising computational simulations, we assessed the binding properties of these compounds within the receptor-binding domain (RBD) and other key regions of the spike protein. Our comparative analysis elucidates the differential binding patterns and identifies promising lead compounds with high binding affinity and favourable interaction profiles. Furthermore, we discuss the implications of these findings for the development of potential therapeutics targeting the SARS-CoV-2 spike protein. Using molecular docking and the Lipinski five rule, this study illustrates possible compounds with strong binding affinities, their molecular interactions, for both naturally occurring and man-made drugs. Computational approach is applied, and it is concluded that, drugs like Withanolide, Dihydroergocristine, Fenebrutinib, and Ergotamine shows binding energies between -8.3 and -9.1 kcal/mol, and are possible candidate for anti covid drug.

Controlled differential evolution based detection of neovascularization on optic disc using support vector machine.

The severe stage of Diabetic Retinopathy (DR) is characterized by the growth of new blood vessels which is called Neovascularization (NV). The abnormally grown blood vessels on the disc are breakable in nature thus the patient is at high risk of sudden blindness. Therefore, the significance of early and accurate detection of Neovascularization on Disc (NVD) should not be neglected. This paper presents an automatic detection of the optic disc using a Controlled Differential Evolution (CDE) algorithm. Further, the Region of Interest (ROI) is created automatically by extending the extreme boundaries of the optic disc by 100 pixels to ensure the presence of NV around the optic disc also. From the ROI so created, blood vessels are segmented using multi-scale Gabor filtering and subsequently, both the morphological and textural features are extracted. Simultaneously, statistical features are directly extracted from the earlier created ROI. Finally, the fundus image is classified by a Support Vector Machine (SVM) using the extracted features from all three feature sets. From each individual image, 16 features are extracted and the feature dimension is reduced to 13 using a sequential backward feature (SBF) selection algorithm. The optimal features are obtained from a total of 205 fundus images, which consists of 99 NVD positive and 106 NVD negative images. This paper attains an average accuracy of 98.75%, the specificity of 100%, the sensitivity of 97.8%, and area under the curve (AUC) as 100% when tested over image selected randomly.