About this Research Topic
Computational drug discovery is an effective strategy to accelerate drug discovery and development. Because of the dramatic increase in the availability of biological macromolecule, small molecule interactions and antibody computational aid design techniques, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery, optimization, preclinical tests and clinical development. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved to be quicker, cheaper and more effective. Recent advances in protein structure prediction with deep learning like AlphaFold again significantly changed the strategies of drug development. What’s more, computational immunology has supported humans to be able develop coronavirus vaccine only 6 months after the first virus identification. Finally, computational approaches are accelerating neoantigen identification and personalized cancer immunotherapy in therapeutic cancer vaccines development.
This Research Topic focuses on important computational methods, platforms, successful applications, benchmarking datasets, benchmarking workflow/pipelines and integrative analysis of high-throughput datasets in this field. The Research Topic covers include, but are not limited to, the followings:
1. Benchmarking datasets and benchmarking workflow/pipelines. Structural dataset/database are still the most important section for computational approach for drug discovery. We welcome database and dataset works to be published to enhance our capability in drug discovery. Meanwhile, workflow and pipeline are also welcomed to integrate different tools and approaches to provide high efficiency of the drug discovery and development. Computational approach to generate disease knowledge including test mining, association, and causal inference are welcomed. We expect all the datasets and pipelines are hosted in Github to enhance the drug research.
2. Computational method for drug target identification and prioritization. In the past decades, biomedical and biology research have significantly increased the data volume and diversity for drug target identification for human diseases (eg: genetic disease, immune disease, metabolic diseases and human cancers). Big data based computational approaches are able to be used across the whole drug discovery pipeline from target identification, mechanism of action inference to identification of novel leads and drug candidates.
3. Computational method in compound development and optimization, clinical trial enhancement, drug repurposing, compound identification and interaction prediction, target validation, computer-aided drug design including ligand-base, structure-based CADD as well as other Docking-based virtual and pharmacophore modeling. Deep learning technique like alphafold have significantly change the way for the drug discovery strategies.
4. Integrative analysis of high-throughput datasets in compound development and optimization, clinical trial enhancement, drug repurposing, compound identification and interaction prediction, target validation, computer-aided drug design including ligand-base, structure-based CADD as well as other Docking-based virtual and pharmacophore modeling. Deep learning technique like alphafold have significantly change the way for the drug discovery strategies.
This Research Topic focuses on important computational methods, platforms, successful applications, benchmarking datasets, benchmarking workflow/pipelines and integrative analysis of high-throughput datasets in this field. The Research Topic covers include, but are not limited to, the followings:
1. Benchmarking datasets and benchmarking workflow/pipelines. Structural dataset/database are still the most important section for computational approach for drug discovery. We welcome database and dataset works to be published to enhance our capability in drug discovery. Meanwhile, workflow and pipeline are also welcomed to integrate different tools and approaches to provide high efficiency of the drug discovery and development. Computational approach to generate disease knowledge including test mining, association, and causal inference are welcomed. We expect all the datasets and pipelines are hosted in Github to enhance the drug research.
2. Computational method for drug target identification and prioritization. In the past decades, biomedical and biology research have significantly increased the data volume and diversity for drug target identification for human diseases (eg: genetic disease, immune disease, metabolic diseases and human cancers). Big data based computational approaches are able to be used across the whole drug discovery pipeline from target identification, mechanism of action inference to identification of novel leads and drug candidates.
3. Computational method in compound development and optimization, clinical trial enhancement, drug repurposing, compound identification and interaction prediction, target validation, computer-aided drug design including ligand-base, structure-based CADD as well as other Docking-based virtual and pharmacophore modeling. Deep learning technique like alphafold have significantly change the way for the drug discovery strategies.
4. Integrative analysis of high-throughput datasets in compound development and optimization, clinical trial enhancement, drug repurposing, compound identification and interaction prediction, target validation, computer-aided drug design including ligand-base, structure-based CADD as well as other Docking-based virtual and pharmacophore modeling. Deep learning technique like alphafold have significantly change the way for the drug discovery strategies.
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