Lead Screening for HIV-1 Integrase (IN) Inhibited by Traditional Chinese Medicine

Hung, Tzu-Chieh; Chen, Kuen‐Bao; Chan, Yueh-Chiu; Chen, Calvin Yu-Chian

doi:10.1155/2014/479367

Cited by 3 publications

(2 citation statements)

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“…Researchers have put a considerable focus on HIV therapy and a lot of compounds have been tested against this pathogen [ 3 , 4 ]. However, a few antiretroviral drugs have been able to slow the disease progression.…”

Section: Introductionmentioning

confidence: 99%

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

et al. 2018

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A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0266-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

et al. 2018

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“…All exhalations, such as breathing, sneezing, coughing, talking, and singing, can be described as pointsource emissions of a turbulent multiphase gas cloud containing respiratory liquid droplets [127]. The motion that propels this cloud is primarily driven by the gas phase rather than the liquid-droplet phase [128]. (b) Fibonacci sequences of LES results, wherein the total pressure P T keeps constant in the stream tube when the ventilation rate is not too small, by analogy to wind pressure P W is decided by the position of the opening; (c) Projected cytometry of total pressure P T ′ at the same opening position of a sealed room model is similar to the P T at opening (ΔP is the tangential dynamic pressure, and the value is the difference between P T and P W ) with significant Marks-Kendall metric, with scatter plots of Trellis-density correlations between studying the impact of varying wind direction on coefficient of variation (CV) in a wind tunnel by innovative utilization of a revolving turntable and wind direction fluctuations.…”

Section: Stochastic Integrationsmentioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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The spread of infectious diseases such as COVID-19 depends on complex fluid dynamics interactions between pathogens and fluid phases, including individual droplets and multiphase clouds. Understanding these interactions is crucial for predicting and controlling disease spread. This applies to human and animal exhalations, such as coughs and sneezes, as well as bursting bubbles that create micron-sized droplets in various indoor and outdoor environments. By exploring case studies in this regard, this study examines the emerging field of fluid dynamics in disease transmission, focusing on multiphase flows, interfacial flows, turbulence, pathogens, human traffic, aerosol transmission, ventilation, and breathing microenvironments. These results indicate that increased ventilation rates and local ventilation methods can effectively reduce the concentration of SARS-CoV-2-laden aerosols in the immediate breathing spaces between individuals. In a displacement-ventilated room, both neutral and unstable conditions were more effective in removing breathed SARS-CoV-2-laden aerosols from the air, regardless of the presence of test subjects. However, stable conditions may increase the risk of infection in individuals living in confined spaces. Thus, the findings of this study are useful for providing practical guidance for managing the spread of airborne infections. HIGHLIGHTS Fluid dynamics affect the transmission of infectious diseases such as COVID-19 This study explored multiphase fluid flow, aerosol dispersion, and respiratory zones Increased ventilation and local methods reduce SARS-CoV-2 aerosol concentration Displacement ventilation eliminates SARS-CoV-2 aerosols under unstable conditions Cramped and damp living environments can increase the risk of transmission of infection GRAPHICAL ABSTRACT

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Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Tarasova

Poroikov

2021

CMC

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: Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others. Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

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Lead Screening for HIV-1 Integrase (IN) Inhibited by Traditional Chinese Medicine

Cited by 3 publications

References 61 publications

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

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