Owing to the rapid improvement of computational methodologies and high-performance computational resources, computer-aided drug design (CADD) has been validated as an efficient and powerful strategy in almost every stage of drug discovery and development.
Generally, CADD can be divided into structure-based drug design (SBDD) and ligand-based drug design (LBDD). Due to the rapid development of crystallography and homology modeling, structure-based virtual screening has emerged as a useful technique to identify potential hits during the early stage of drug discovery. LBDD strategies based on available information of known bioactive molecules, such as QSAR (Quantitative Structure-Activity Relationship) analysis, scaffold hopping, or pharmacophore modeling, are also widely used for hit optimization and activity prediction. In addition, computational techniques like quantum chemistry calculation, molecular dynamics (MD) simulations, and elastic network models can be used to study protein catalytic mechanism, conformational transition, and allosteric regulations at an atomic level of detail, which provide useful information for mechanism-based drug design.
Recently, with the development of machine learning theory and the accumulation of pharmacological data, artificial intelligence (AI), a powerful data mining technology, has been widely used in various fields of drug design, including virtual screening, de novo drug design, QSAR analysis, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.
This Research Topic aims to cover continuous and current advancements as well as applications in CADD, including hit identification and optimization, target identification and validation, and mechanism studies of important targets. Through this Research Topic, we want to provide a platform to increase readers’ and researchers’ understanding of computational techniques in drug design, and we welcome the submission of Original Research, Review, Mini-Review, and Perspective articles in themes including, but not limited to:
• Successful applications of CADD strategies during the discovery and development of novel drug candidates;
• Mechanism study of important targets, including protein allosteric regulation, conformational dynamics, and catalytic mechanism;
• Studies related to the development of methodologies in CADD
Owing to the rapid improvement of computational methodologies and high-performance computational resources, computer-aided drug design (CADD) has been validated as an efficient and powerful strategy in almost every stage of drug discovery and development.
Generally, CADD can be divided into structure-based drug design (SBDD) and ligand-based drug design (LBDD). Due to the rapid development of crystallography and homology modeling, structure-based virtual screening has emerged as a useful technique to identify potential hits during the early stage of drug discovery. LBDD strategies based on available information of known bioactive molecules, such as QSAR (Quantitative Structure-Activity Relationship) analysis, scaffold hopping, or pharmacophore modeling, are also widely used for hit optimization and activity prediction. In addition, computational techniques like quantum chemistry calculation, molecular dynamics (MD) simulations, and elastic network models can be used to study protein catalytic mechanism, conformational transition, and allosteric regulations at an atomic level of detail, which provide useful information for mechanism-based drug design.
Recently, with the development of machine learning theory and the accumulation of pharmacological data, artificial intelligence (AI), a powerful data mining technology, has been widely used in various fields of drug design, including virtual screening, de novo drug design, QSAR analysis, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.
This Research Topic aims to cover continuous and current advancements as well as applications in CADD, including hit identification and optimization, target identification and validation, and mechanism studies of important targets. Through this Research Topic, we want to provide a platform to increase readers’ and researchers’ understanding of computational techniques in drug design, and we welcome the submission of Original Research, Review, Mini-Review, and Perspective articles in themes including, but not limited to:
• Successful applications of CADD strategies during the discovery and development of novel drug candidates;
• Mechanism study of important targets, including protein allosteric regulation, conformational dynamics, and catalytic mechanism;
• Studies related to the development of methodologies in CADD
