Cancer is still a major public health concern, and it is regarded as one of the leading causes of mortality globally. Despite significant advances in biotechnologies, developing practical and innovative small molecule drugs remains a difficult, time-consuming, and expensive task that necessitates collaborations from many experts in multidisciplinary fields such as computational biology, drug metabolism, and clinical research, among others. Therefore, new drug development procedures that save time and cost while increasing efficiency are in high demand. In silico screening in combination with molecular simulations, has become an increasingly important aspect of modern drug development processes.
Understanding ligand-protein interaction is critical in all areas of drug design and discovery. Computational approaches, such as molecular docking, molecular dynamics simulations, pharmacophore modeling, and QSAR, etc. are efficient tools for obtaining insights on structure-function relationships for small molecules and/or medicinal compounds with target proteins, and are widely used in the identification and optimization of leads. The goal of the drug discovery process is to predict a drug candidate's metabolic fate in order to build a link between pharmacodynamics and pharmacokinetics and to identify the drug candidate's possible toxicity. The advancement of in silico techniques in recent years has enabled researchers to collect more trustworthy data.
This Research Topic will focus on the use and application of computational methods that can aid in the drug design of medicinal compounds targeting various proteins for cancer management, which is a critical requirement in the pharmaceutical sectors. We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:
• Identification of druggable interfaces/hotspots, and binding site identification
• Receptor-based virtual screening and molecular docking
• Molecular dynamics simulation
• Druglikeness, pharmacokinetics and physiochemical properties prediction
• Structure-Based Drug Discovery
• Anti-cancer drug discovery using artificial intelligence
• Computational approaches applied to medicinal compounds
• QSAR and 3D-QSAR
Cancer is still a major public health concern, and it is regarded as one of the leading causes of mortality globally. Despite significant advances in biotechnologies, developing practical and innovative small molecule drugs remains a difficult, time-consuming, and expensive task that necessitates collaborations from many experts in multidisciplinary fields such as computational biology, drug metabolism, and clinical research, among others. Therefore, new drug development procedures that save time and cost while increasing efficiency are in high demand. In silico screening in combination with molecular simulations, has become an increasingly important aspect of modern drug development processes.
Understanding ligand-protein interaction is critical in all areas of drug design and discovery. Computational approaches, such as molecular docking, molecular dynamics simulations, pharmacophore modeling, and QSAR, etc. are efficient tools for obtaining insights on structure-function relationships for small molecules and/or medicinal compounds with target proteins, and are widely used in the identification and optimization of leads. The goal of the drug discovery process is to predict a drug candidate's metabolic fate in order to build a link between pharmacodynamics and pharmacokinetics and to identify the drug candidate's possible toxicity. The advancement of in silico techniques in recent years has enabled researchers to collect more trustworthy data.
This Research Topic will focus on the use and application of computational methods that can aid in the drug design of medicinal compounds targeting various proteins for cancer management, which is a critical requirement in the pharmaceutical sectors. We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:
• Identification of druggable interfaces/hotspots, and binding site identification
• Receptor-based virtual screening and molecular docking
• Molecular dynamics simulation
• Druglikeness, pharmacokinetics and physiochemical properties prediction
• Structure-Based Drug Discovery
• Anti-cancer drug discovery using artificial intelligence
• Computational approaches applied to medicinal compounds
• QSAR and 3D-QSAR
