Gotowa bibliografia na temat „Virtual screening of compounds and experimental validation”

Virtual screening, or VS, is emerging as a valuable tool in discovering new candidate inhibitors for many biologically relevant targets including the many chemotherapeutic targets that play key roles in cell signaling pathways. However, despite the great advances made in the field thus far, VS is still in constant development with a relatively low success rate that needs to be improved by parallel experimental validation methods. This chapter reviews the recent advances in VS, focusing on the range and type of computational methods and their successful applications in drug discovery. The chapter also discusses both the advantages and limitations of the various techniques used in VS and outlines a number of future directions in which the field may progress.

Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.

The global spread of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), which causes the disease COVID-19, has increased drastically since the first cases in Wuhan, People's Republic of China, at the end of 2019. There is no single drug that can be used specifically to treat COVID. The crucial stage in the drug development process is screening huge libraries of bioactive molecules against a biological target, usually a receptor or a protein. Virtual Screening (VS) has become a valuable tool in the drug development process as it allows for efficient in silico searches of millions of compounds, resulting in higher yields of possible therapeutic leads, and is cost-effective. The spread of the SARS-CoV-2 virus presents a major threat to world health and has resulted in a global crisis because of the high mortality rate and absence of clinically authorised treatments and vaccines for COVID-19. Finding effective drugs or repurposing available antiviral drugs is a critical need in the fight against COVID-19. VS can be classified as either Structural-Based Virtual Screening or Ligand-Based Virtual Screening. VS techniques have been widely applied in the field of antiviral drug design and have aided in the identification of new compounds as possible anti-viral drugs. Both LBVS and SBVS approaches have proved extremely helpful in identifying several prospective anti-viral drugs with nanomolar range. VS, in contrast to experimental approaches, is quick and cost-effective on the one side but has low prediction accuracy on the other.

The current climate surrounding adolescent marijuana use in the U.S. is facing unprecedented circumstances. Rates of daily use are at or near all-time highs and perceptions of risk are at an all-time low in the history of the Monitoring the Future study among 8th, 10th, and 12th graders. These rates are occurring despite research demonstrating worse long-term health outcomes associated with earlier age of marijuana use onset and increasing THC levels among marijuana products. As a result, there is an urgent need to identify risk factors that may represent screening markers of risk or targets f