It has been reported that the total investment required for discovering and developing new drugs averages USD 2 billion, the whole process takes years even decades. The cost for the anti-cancer drug is much more than this. In recent years, computational drug development methods have emerged to address computational-experimental challenges to add immediate value to drug development pipelines. In the postgenomic era, the application of computational methods on drug design and development has considerably extended. The range spans almost all stages in the drug discovery pipeline, from target identification to lead discovery, lead optimization, then to preclinical or clinical trials. When utilized effectively, computational techniques can shorten timelines and enhance the cost-effectiveness of drug development. As a result, it is fundamentally important for the development of new computational approaches in anticancer drug design and drug development.
Over the last few decades, diverse computational technics, such as machine learning techniques, even deep learning techniques, have been widely used in computational drug design and development. But it still has a long way to go for the anti-cancer drug development area. To expand the scope and ease of applicability of machine learning in the anti-cancer drug industry, it is highly desirable to propose new computational models or new computational algorithms, and more importantly, to make progress toward using artificial intelligence (AI). With the increases in computational power and the development of exciting new methods, computational techniques are promising to occupy a more central place in future drug discovery efforts.
This Research Topic will target computational methods and applications that are relevant for anti-cancer drug discovery and drug development. The traditional anti-cancer drug development suffers from huge costs and long turn-round time. Computational techniques were developed in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. Articles on anti-cancer drug development and drug discovery are welcomed for this Research Topic, including but not limited to target identification, compound screening, anti-cancer drug safety and efficacy test, anti-cancer drug repositioning, new indications for anti-cancer drug discovery using computational methods. We also encourage authors to make their codes and experimental data available to the public, which would make our topic more infusive and attractive.
• Computational methods in target identification, pharmacophore mapping, protein structure prediction.
• Molecular docking and structure-based virtual screening
• pharmacophore modeling • QSAR (2D, 3D, or 4D)
• In silico absorption, distribution, metabolism, and excretion (ADME)
• In silico toxicology
• Computer-aided drug repurposing
• Drug response prediction
• Pharmacokinetic simulations
Note: manuscripts based solely on in silico techniques without validation (independent cohort or biological validation in vitro or in vivo) will not be considered for review.
It has been reported that the total investment required for discovering and developing new drugs averages USD 2 billion, the whole process takes years even decades. The cost for the anti-cancer drug is much more than this. In recent years, computational drug development methods have emerged to address computational-experimental challenges to add immediate value to drug development pipelines. In the postgenomic era, the application of computational methods on drug design and development has considerably extended. The range spans almost all stages in the drug discovery pipeline, from target identification to lead discovery, lead optimization, then to preclinical or clinical trials. When utilized effectively, computational techniques can shorten timelines and enhance the cost-effectiveness of drug development. As a result, it is fundamentally important for the development of new computational approaches in anticancer drug design and drug development.
Over the last few decades, diverse computational technics, such as machine learning techniques, even deep learning techniques, have been widely used in computational drug design and development. But it still has a long way to go for the anti-cancer drug development area. To expand the scope and ease of applicability of machine learning in the anti-cancer drug industry, it is highly desirable to propose new computational models or new computational algorithms, and more importantly, to make progress toward using artificial intelligence (AI). With the increases in computational power and the development of exciting new methods, computational techniques are promising to occupy a more central place in future drug discovery efforts.
This Research Topic will target computational methods and applications that are relevant for anti-cancer drug discovery and drug development. The traditional anti-cancer drug development suffers from huge costs and long turn-round time. Computational techniques were developed in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. Articles on anti-cancer drug development and drug discovery are welcomed for this Research Topic, including but not limited to target identification, compound screening, anti-cancer drug safety and efficacy test, anti-cancer drug repositioning, new indications for anti-cancer drug discovery using computational methods. We also encourage authors to make their codes and experimental data available to the public, which would make our topic more infusive and attractive.
• Computational methods in target identification, pharmacophore mapping, protein structure prediction.
• Molecular docking and structure-based virtual screening
• pharmacophore modeling • QSAR (2D, 3D, or 4D)
• In silico absorption, distribution, metabolism, and excretion (ADME)
• In silico toxicology
• Computer-aided drug repurposing
• Drug response prediction
• Pharmacokinetic simulations
Note: manuscripts based solely on in silico techniques without validation (independent cohort or biological validation in vitro or in vivo) will not be considered for review.
