Titlebook: Current Trends in Computational Modeling for Drug Discovery; Supratik Kar,Jerzy Leszczynski Book 2023 The Editor(s) (if applicable) and Th

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https://doi.org/10.1007/978-3-658-10567-9gical and pathological disease conditions. HDAC6 and HDAC10 are involved in different signaling pathways associated with several neurological disorders, various cancers at early as well as advanced stages, rare diseases, immunological conditions, etc. Thus, targeting these two enzymes has been found

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脊椎動物

https://doi.org/10.1007/978-3-658-10567-9antiviral drugs for treatment. Since the 1950s, new viral illnesses including AIDS, Hepatitis, and coronavirus infections like SARS, MERS, and COVID-19 have periodically emerged, posing a challenge to the development of antiviral drugs. The creation of computer models is an interactive, iterative pr

組成

https://doi.org/10.1007/978-3-658-10567-9igh fatality rate. With time, the world has faced numerous outbreaks in various regions such as Malaysia, Bangladesh, Philippines, and India. In this chapter, we have summarized experimentally tested antivirals and computational approaches to predict potential inhibitors against NiV. Various studies

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https://doi.org/10.1007/978-3-658-10567-9nti-HIV drugs remains a major cause of concern, necessitating a regimen of highly active antiretroviral therapy (HAART), which consists of a combination of multiple drugs for long-term clinical benefit. Clearly, the rapid development of novel molecules that can help change the present regimen to new

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Favorable

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https://doi.org/10.1007/978-3-658-10567-9pects like reproducibility, less ethical complications, no animal use and reduced time are some of the reasons why?researchers nowadays are shifting toward the in silico approaches for prediction. Quantitative Structure–Activity Relationship (QSAR) is one of the most commonly used in silico approach