Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads.

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview.

Background: Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection.

An in silico pharmacological approach toward the discovery of potent inhibitors to combat drug resistance HIV-1 protease variants.

Protease inhibitors (PIs) are crucial drugs in highly active antiretroviral therapy for human immunodeficiency virus-1 (HIV-1) infections. However, resistance owing to mutations challenge the long-term efficacy in the medication of HIV-1-infected individuals. Lopinavir (LPV) and darunavir (DRV), two second-generation drugs are the most potent among PIs, hustling the drug resistance when mutations occur in the active and nonactive site of the protease (PR).

De Novo Design of Ligands Using Computational Methods.

De novo design technique is complementary to high-throughput virtual screening and is believed to contribute in pharmaceutical development of novel drugs with desired properties at a very low cost and time-efficient manner. In this chapter, we outline the basic de novo design concepts based on computational methods with an example.

Molecular dynamics simulation of the effects of single (S221P) and double (S221P and K216E) mutations in the hemagglutinin protein of influenza A H5N1 virus: a study on host receptor specificity.

Avian influenza viruses of subtype H5N1 circulating in animals continue to pose threats to human health. The binding preference of the viral surface protein hemagglutinin (HA) to sialosaccharides of receptors is an important area for understanding mutations in the receptor binding site that could be the cause for avian-to-human transmission. In the present work, we studied the effect of two receptor binding site mutations, S221P singly and in combination with another mutation K216E in the HA protein of influenza A H5N1 viruses.