Identification of drug sites for Human immunodeficiency virus (HIV) through bioinformatic and machine learning techniques.

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Online Journal of Bioinformatics ©

Volume 19 (2):56-66, 2018.

Potential drug target sites of HIV identified by bioinformatics and intelligent machine learning techniques.

Dr Anubha Dubey.

Computational Biology, Gayatri Nagar, Katni, M.P. India.

ABSTRACT

Dubet A., Potential drug target sites of HIV identified by bioinformatics and intelligent machine learning techniques. Onl J Bioinform 19 (2):56-66, 2018. Author reviews recent In silico identification of drug target sites for HIV by HIV-1 and HIV-2 structural and regulatory proteins, HIV miRNA/RNAi and siRNA based drugs, subcellular and membrane protein sites through bioinformatics and machine learning. Discovery includes assessment of experimental and theoretical mechanistic and pharmacological studies. Potential drug target sites identified by machine learning techniques of great accuracy are discussed. In this review differences between vpu and vpx genes for potential drug targeting for HIV are discussed. Intelligent machine learning can be used to validate target sites for HIV/AIDS to reduce attrition rates for later stages of drug development. Molecular barcoding can be used to identify mutant spectrum changes in infected hosts. However future drug and vaccine studies need to be validated in animal models, as subtle differences can have a significant impact on experimental outcome. Quasi-species theories may soon move from the laboratory to control and treatment for HIV/AIDS. As new therapeutics are identified or validated, databases are further improved.

Keywords: Therapeutics, target, Disease, HIV/AIDS, Machine learning.

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