Structure-based virtual screening, particularly through molecular docking, has become an indispensable tool in biochemistry, medicinal chemistry, and drug discovery. This approach allows researchers to efficiently screen vast libraries of compounds, identifying potential candidates for further experimental validation. Despite its widespread use, the accuracy of virtual screening is often hindered by the limitations of current scoring functions, which can lead to high rates of false positives and negatives. Recent studies have highlighted the need for more reliable methods to rank docking poses and predict binding affinities accurately. While some progress has been made with the introduction of additional scoring functions and post-docking tools, there remains a significant gap in achieving consistent and reliable predictions. Addressing these challenges is crucial for enhancing the efficiency and success rate of drug discovery processes.
This research topic aims to explore and develop innovative approaches to improve the performance of virtual screening. The primary objectives include refining scoring functions, enhancing structural filtration techniques, and improving the prediction of physicochemical properties and pharmacological activities. By addressing these areas, the research seeks to answer critical questions about the accuracy and reliability of current virtual screening methods and to test hypotheses related to the optimization of ligand selection processes.
To gather further insights in the realm of virtual screening and its associated methodologies, we welcome articles addressing, but not limited to, the following themes:
• Identification of small-molecule inhibitors/activators through molecular docking and virtual screening
• Using scoring functions for correct rank-ordering of docked poses
• Employing structural filtration for the selection of specifically bound ligands
• Selection of ligands with desired properties based on the calculation of physicochemical parameters
• Prediction of biological activities and adverse effects, including QSAR modeling, for candidates selected by docking
• Development of new approaches to improving the performance of virtual screening.
Structure-based virtual screening, particularly through molecular docking, has become an indispensable tool in biochemistry, medicinal chemistry, and drug discovery. This approach allows researchers to efficiently screen vast libraries of compounds, identifying potential candidates for further experimental validation. Despite its widespread use, the accuracy of virtual screening is often hindered by the limitations of current scoring functions, which can lead to high rates of false positives and negatives. Recent studies have highlighted the need for more reliable methods to rank docking poses and predict binding affinities accurately. While some progress has been made with the introduction of additional scoring functions and post-docking tools, there remains a significant gap in achieving consistent and reliable predictions. Addressing these challenges is crucial for enhancing the efficiency and success rate of drug discovery processes.
This research topic aims to explore and develop innovative approaches to improve the performance of virtual screening. The primary objectives include refining scoring functions, enhancing structural filtration techniques, and improving the prediction of physicochemical properties and pharmacological activities. By addressing these areas, the research seeks to answer critical questions about the accuracy and reliability of current virtual screening methods and to test hypotheses related to the optimization of ligand selection processes.
To gather further insights in the realm of virtual screening and its associated methodologies, we welcome articles addressing, but not limited to, the following themes:
• Identification of small-molecule inhibitors/activators through molecular docking and virtual screening
• Using scoring functions for correct rank-ordering of docked poses
• Employing structural filtration for the selection of specifically bound ligands
• Selection of ligands with desired properties based on the calculation of physicochemical parameters
• Prediction of biological activities and adverse effects, including QSAR modeling, for candidates selected by docking
• Development of new approaches to improving the performance of virtual screening.