Infectious diseases with the potential to cause a global pandemic have consistently emerged and spread throughout history. Viruses are constantly evolving due to their high rates of recombination, and some of them are highly virulent and capable of producing a worldwide pandemic. Humanity has already been tormented by major pandemics and epidemics, including plague, cholera, influenza, ebola, polio, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV).
In the present era, COVID-19 poses a global concern owing to its infectious spread, which has resulted in an unprecedented wave of morbidity and mortality. Most recently, the monkeypox virus has emerged as a threat to public health. Thus, the advent of viral infections as an epidemic threat has drawn attention to the development of medications to combat these growing diseases.
In the modern world, in silico approaches such as virtual screening of ultra-large libraries, scaffold-based drug design, fragment-based drug discovery, QSAR/pharmacophore modeling, molecular dynamics simulation, quantum mechanics/molecular mechanics (QM/MM), Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), Molecular Mechanics Generalized Born Surface Area (MM-GBSA), and free energy perturbation (FEP) methods are essential in the field of drug discovery as they save time and cost. It is also worth acknowledging the more traditional computational approaches such as ligand-based drug design, structure-based drug design, quantitative structure-activity and structure-property relationships. Furthermore, rapid developments in the fields of artificial intelligence (AI) and machine learning are proving to be invaluable in drug design and development.
Therefore, an in silico drug design approach followed by experimental and virtual testing is an effective method to identify antivirals for life-threatening viruses. As such, this Research Topic will include research papers and reviews on the design and development of antiviral agents for viral infectious diseases. We are delighted to invite innovative contributions which include, but are not limited to:
• Screening and identification of candidates for infectious diseases
• Structure-based and ligand-based drug design
• AI, machine and deep learning approaches
• QSAR/pharmacophore modeling
• Drug repurposing, vaccine development and antiviral design
• Biophysical or in vitro evaluation of identified candidates
• Antiviral efficacy of lead molecules
• In vivo assessment of potent candidates to minimize viral load
• The exploration of unidentified (hypothetical) proteins from viral infectious diseases.
Infectious diseases with the potential to cause a global pandemic have consistently emerged and spread throughout history. Viruses are constantly evolving due to their high rates of recombination, and some of them are highly virulent and capable of producing a worldwide pandemic. Humanity has already been tormented by major pandemics and epidemics, including plague, cholera, influenza, ebola, polio, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV).
In the present era, COVID-19 poses a global concern owing to its infectious spread, which has resulted in an unprecedented wave of morbidity and mortality. Most recently, the monkeypox virus has emerged as a threat to public health. Thus, the advent of viral infections as an epidemic threat has drawn attention to the development of medications to combat these growing diseases.
In the modern world, in silico approaches such as virtual screening of ultra-large libraries, scaffold-based drug design, fragment-based drug discovery, QSAR/pharmacophore modeling, molecular dynamics simulation, quantum mechanics/molecular mechanics (QM/MM), Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), Molecular Mechanics Generalized Born Surface Area (MM-GBSA), and free energy perturbation (FEP) methods are essential in the field of drug discovery as they save time and cost. It is also worth acknowledging the more traditional computational approaches such as ligand-based drug design, structure-based drug design, quantitative structure-activity and structure-property relationships. Furthermore, rapid developments in the fields of artificial intelligence (AI) and machine learning are proving to be invaluable in drug design and development.
Therefore, an in silico drug design approach followed by experimental and virtual testing is an effective method to identify antivirals for life-threatening viruses. As such, this Research Topic will include research papers and reviews on the design and development of antiviral agents for viral infectious diseases. We are delighted to invite innovative contributions which include, but are not limited to:
• Screening and identification of candidates for infectious diseases
• Structure-based and ligand-based drug design
• AI, machine and deep learning approaches
• QSAR/pharmacophore modeling
• Drug repurposing, vaccine development and antiviral design
• Biophysical or in vitro evaluation of identified candidates
• Antiviral efficacy of lead molecules
• In vivo assessment of potent candidates to minimize viral load
• The exploration of unidentified (hypothetical) proteins from viral infectious diseases.
