About this Research Topic
Computational methods represent today a great support for experimental research. The most sophisticated computational techniques are able to predict interactions and functionalities which then prove to be consistent when compared with the experimental reality. The Covid-19 pandemic, that we are still experiencing, has generated the interest and study of all researchers in the world who have made their expertise available to contribute to the solution of this global problem. This pandemic has shown how correctly applied computational approaches can help the understanding of the structural basis underlying possible coronavirus inhibition mechanisms and how they may contribute to accelerate the discovery of novel treatment methods.
Although the release of very effective vaccines is helping controlling this disease and reducing its burden in the worldwide population, there is still a lack of effective, safe and broad-spectrum antiviral drugs to treat infected patients and also to stem future epidemics. The rapid development of new, low-cost, antiviral drugs will help emerging developing countries where there is a limited diffusion of vaccination therapy. Thanks to the solution of the molecular structures composing the SARS-CoV-2 virion, many are the targets that have been offered to molecular simulators who have designed various classes of molecules, peptides or have selected proteins and antibodies, with the aim of stemming the spread of this threatening coronavirus.
This Research Topic wants to combine the not yet published contributions of those researchers that are involved in the use or development of computational approaches applied to drug discovery. The aim is to provide an overview and a vision of what simulation is able to offer today, as a help and a guide for experimentalists. This Research Topic will collect various types of computational researches, possibly validated by experimental approaches, that have used as a target the coronavirus proteins that make up the virus and allow the SARS-CoV-2 infection. Areas to be covered in this Research Topic may include, but are not limited to:
• Molecular modelling, molecular docking and/or molecular dynamics simulations studies guiding the discovery of potential inhibitors.
• Computational drug repurposing strategies.
• Virtual screening of ligand libraries.
• Drug discovery strategies applying machine learning approaches or related computational techniques.
• Computational design and experimental evaluation of potential inhibitors.
Although the release of very effective vaccines is helping controlling this disease and reducing its burden in the worldwide population, there is still a lack of effective, safe and broad-spectrum antiviral drugs to treat infected patients and also to stem future epidemics. The rapid development of new, low-cost, antiviral drugs will help emerging developing countries where there is a limited diffusion of vaccination therapy. Thanks to the solution of the molecular structures composing the SARS-CoV-2 virion, many are the targets that have been offered to molecular simulators who have designed various classes of molecules, peptides or have selected proteins and antibodies, with the aim of stemming the spread of this threatening coronavirus.
This Research Topic wants to combine the not yet published contributions of those researchers that are involved in the use or development of computational approaches applied to drug discovery. The aim is to provide an overview and a vision of what simulation is able to offer today, as a help and a guide for experimentalists. This Research Topic will collect various types of computational researches, possibly validated by experimental approaches, that have used as a target the coronavirus proteins that make up the virus and allow the SARS-CoV-2 infection. Areas to be covered in this Research Topic may include, but are not limited to:
• Molecular modelling, molecular docking and/or molecular dynamics simulations studies guiding the discovery of potential inhibitors.
• Computational drug repurposing strategies.
• Virtual screening of ligand libraries.
• Drug discovery strategies applying machine learning approaches or related computational techniques.
• Computational design and experimental evaluation of potential inhibitors.
Keywords: Covid-19, biomolecules, bioactive compounds, SARS-CoV-2, computational approaches, drug discovery, simulation, Molecular modelling, molecular dynamics, Virtual screening, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
