About this Research Topic
In the face of the relentless threat posed by highly pathogenic RNA viruses, such as SARS-CoV-2, HIV, and avian influenza, the quest for innovative antiviral therapies has never been more urgent. These viruses, characterized by their rapid mutation rates and their ability to evade host immune responses, pose significant global public health challenges. Traditional drug discovery approaches have struggled to keep pace with the evolving nature of these pathogens, highlighting the need for transformative technologies that can accelerate the identification and evaluation of novel therapeutic agents.
Deep learning, a subset of artificial intelligence, has revolutionized data-driven research in recent years. By harnessing the power of advanced neural networks, deep learning algorithms can process vast amounts of complex biological data, uncover hidden patterns, and predict outcomes with unprecedented accuracy. In the context of antiviral drug discovery, deep learning has emerged as a promising tool for identifying novel targets, optimizing drug candidates, and predicting their efficacy and safety profiles.
One such target of immense interest is the N6-methyladenosine (m6A) modification, a ubiquitous post-transcriptional modification found in eukaryotic mRNAs. By regulating gene expression at the levels of RNA stability, translation efficiency, and splicing, m6A plays a pivotal role in many biological processes, including viral replication. Targeting m6A modification thus offers a novel strategy for developing antiviral drugs that can disrupt viral gene expression and replication cycles.
In this special issue, we aim to showcase the latest advances in deep learning-assisted discovery and evaluation of novel anti-RNA virus drugs targeting m6A modification. We invite contributions that explore the intersection of deep learning, bioinformatics, structural biology, and virology to address the pressing need for effective treatments against highly pathogenic RNA viruses. From the identification of novel m6A-modulating compounds to the in-depth characterization of their antiviral mechanisms, we encourage submissions that push the boundaries of current knowledge and pave the way for the next generation of antiviral therapies.
Through this special issue, we hope to foster collaborations among researchers from diverse fields, accelerate the pace of antiviral drug discovery, and ultimately contribute to the global effort to combat the threat posed by highly pathogenic RNA viruses.
The Research Topic Will Include:
AI-assisted Drug Design and Discovery, Molecular Dynamics Simulation, and Evaluation of Antiviral Activity
m6A Modification in Gene Expression and Immunopharmacology
Antiviral Immunotherapy Approaches in Immunopharmacology
By stimulating collaboration and knowledge-sharing, we aim to fuel biomedical research progress and potentially unveil novel therapeutic avenues.
Deep learning, a subset of artificial intelligence, has revolutionized data-driven research in recent years. By harnessing the power of advanced neural networks, deep learning algorithms can process vast amounts of complex biological data, uncover hidden patterns, and predict outcomes with unprecedented accuracy. In the context of antiviral drug discovery, deep learning has emerged as a promising tool for identifying novel targets, optimizing drug candidates, and predicting their efficacy and safety profiles.
One such target of immense interest is the N6-methyladenosine (m6A) modification, a ubiquitous post-transcriptional modification found in eukaryotic mRNAs. By regulating gene expression at the levels of RNA stability, translation efficiency, and splicing, m6A plays a pivotal role in many biological processes, including viral replication. Targeting m6A modification thus offers a novel strategy for developing antiviral drugs that can disrupt viral gene expression and replication cycles.
In this special issue, we aim to showcase the latest advances in deep learning-assisted discovery and evaluation of novel anti-RNA virus drugs targeting m6A modification. We invite contributions that explore the intersection of deep learning, bioinformatics, structural biology, and virology to address the pressing need for effective treatments against highly pathogenic RNA viruses. From the identification of novel m6A-modulating compounds to the in-depth characterization of their antiviral mechanisms, we encourage submissions that push the boundaries of current knowledge and pave the way for the next generation of antiviral therapies.
Through this special issue, we hope to foster collaborations among researchers from diverse fields, accelerate the pace of antiviral drug discovery, and ultimately contribute to the global effort to combat the threat posed by highly pathogenic RNA viruses.
The Research Topic Will Include:
AI-assisted Drug Design and Discovery, Molecular Dynamics Simulation, and Evaluation of Antiviral Activity
m6A Modification in Gene Expression and Immunopharmacology
Antiviral Immunotherapy Approaches in Immunopharmacology
By stimulating collaboration and knowledge-sharing, we aim to fuel biomedical research progress and potentially unveil novel therapeutic avenues.
Keywords: Deep Learning, m6A Modification, Anti-RNA Virus, Novel Drug Discovery, Evaluation of Antiviral Activity
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.
