Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short span of time. The present review is an overview based on some applications of Machine Learning based tools such as GOLD, DeepPVP, LIBSVM, etc and the algorithms involved such as support vector machine (SVM), random forest (RF), decision trees and artificial neural networks (ANN) etc in the various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure-based virtual screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intesti-nal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF model in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1 by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predicts flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery in order to model small-molecule drugs, Gene Biomarkers, and identifying the novel drug targets for various diseases.
Diagnosis and identification of viruses is an important component of diagnostic virology laboratory. Although various modes of diagnostic methods are now available at disposal, a vast majority of the diseases across the globe remain undiagnosed. This is largely due to the overlapping undifferentiated set of symptoms across myriad set of RNA and DNA viral diseases. As such, it becomes critical to take into consideration several factors for viral diagnosis ranging from the type and quality of specimen collected, time of specimen collection, mode of transport, accuracy, specificity, sensitivity, and the type of diagnostic method used. This chapter broadly emphasizes various methods on diagnostic virology ranging from the classical methods of diagnosis to the most recently developed molecular methods of detection of virus.
Vascular Endothelial Growth Factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identi ed in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential Vascular Endothelial Growth Factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumour cells of the ovary and to examine for an effectiveness of identi ed inhibitor for the treatment of ovarian cancer using various in-silico approaches. 12 established VEGF inhibitors were collected from various literature. The compound AEE788 displays the great a nity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high a nity. Among the 80 Virtual screened compounds, CID 88265020, explicates much better a nity than established compound AEE788. Based on Molecular Dynamics Simulation, pharmacophore and comparative toxicity analysis of both the best-established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 have a high a nity with the lowest re-rank score, and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in Ovarian Cancer.
Background: Originating from the abnormal growth of neuroblasts, pediatric neuroblastoma affects the age group below 15 years. It is an aggressive heterogenous cancer with a high morbidity rate. Biological marker GD2 synthesised by the GD2 gene acts as a powerful predictor of neuroblastoma cells. GD2 gangliosides are sialic acid-containing glycosphingolipids. Differential expression during brain development governs the function of the GD2. The present study explains the interaction of the GD2 with its established inhibitors and discovers the compound having a high binding affinity against the target protein. Technically, during the development of new compounds through docking studies, the best drug among all pre-exist inhibitors was filtered. Hence in reference to the best docked compound, the study proceeded further. Methodology: The In silico approach provides a platform to determine and establish potential inhibitor against GD2 in Pediatric neuroblastoma. The 3D structure of GD2 protein was modelled by homology base fold methods using Smith-Watermans’ Local alignment. A total of 18 established potent compounds were subjected to molecular docking and Etoposide (CID: 36462) manifested the highest affinity. The similarity search presented 336 compounds similar to Etoposide. Results: Through virtual screening, the compound having PubChem ID 10254934 showed a better affinity towards GD2 than the established inhibitor. The comparative profiling of the two compounds based on various interactions such as H-bond interaction, aromatic interactions, electrostatic interactions and ADMET profiling and toxicity studies were performed using various computational tools. Conclusion: The docking separated the virtual screened drug (PubChemID: 10254934) from the established inhibitor with a better re-rank score of -136.33. The toxicity profile of the virtual screened drug was also lesser (less lethal) than the established drug. The virtual screened drug was observed to be bioavailable as it does not cross the blood-brain barrier. Conclusively, the virtual screened compound obtained in the present investigation is better than the established inhibitor and can be further augmented by In vitro analysis, pharmacodynamics and pharmacokinetic studies.
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