With the advancement of artificial intelligence (AI) and machine learning methods, many science and engineering challenges and problems can now be tackled and solved through new computing paradigms. Drug and pharmaceutical studies provide excellent playgrounds and test fields for AI and machine learning methods to exercise their maximum potential. To supply pharmacologically active compounds with a specific function, drug design is crucial to the drug discovery and development process. The idea of Computer-Aided Drug Design was first coined in the '80s, when the capabilities of both hardware and software were limited. Since the starting of the new century, drug discovery, design and repositioning using AI and machine learning approaches benefits computer-aided pharmaceutical research at all stages of the drug development cycle and attract the attention of researchers from medical, pharmaceutical, biochemical, and other related fields.
This Research Topic aims to cover recent advancements in drug discovery, design and repositioning with a special focus on using artificial intelligence and machine learning in service of the analysis and interpretation of drug target affinity/interaction and protein ligand interaction. We would like to receive contributions of novel AI and machine learning methods and applications in both structure-based and ligand-based drug design, and in silico development of drug repositioning with possible in vitro and/or in vivo proof of the concepts.
Themes covered in this Research Topic include, but are not limited to:
o De novo drug discovery and finding
o Drug molecule and disease protein mapping
o Binding affinity prediction
o Drug-Drug Interaction (DDI)
o Drug-Target Interaction (DTI)
o Protein-Protein Interaction (PPI)
o Molecular Docking
o Biospecific and multitarget molecule design
o Virtual screening
o Innovative encapsulation and delivery
o Clinical trial analysis and design
o Intelligent drug experiment process design
o Reaction yield prediction
o Retrosynthesis pathway prediction
o Novel therapeutic use prediction
o Toxicity, bioactivity prediction
o Open drug database mining
o AI in nanomedicine
o Bioinformatics in drug design
o Knowledge management in drug design
With the advancement of artificial intelligence (AI) and machine learning methods, many science and engineering challenges and problems can now be tackled and solved through new computing paradigms. Drug and pharmaceutical studies provide excellent playgrounds and test fields for AI and machine learning methods to exercise their maximum potential. To supply pharmacologically active compounds with a specific function, drug design is crucial to the drug discovery and development process. The idea of Computer-Aided Drug Design was first coined in the '80s, when the capabilities of both hardware and software were limited. Since the starting of the new century, drug discovery, design and repositioning using AI and machine learning approaches benefits computer-aided pharmaceutical research at all stages of the drug development cycle and attract the attention of researchers from medical, pharmaceutical, biochemical, and other related fields.
This Research Topic aims to cover recent advancements in drug discovery, design and repositioning with a special focus on using artificial intelligence and machine learning in service of the analysis and interpretation of drug target affinity/interaction and protein ligand interaction. We would like to receive contributions of novel AI and machine learning methods and applications in both structure-based and ligand-based drug design, and in silico development of drug repositioning with possible in vitro and/or in vivo proof of the concepts.
Themes covered in this Research Topic include, but are not limited to:
o De novo drug discovery and finding
o Drug molecule and disease protein mapping
o Binding affinity prediction
o Drug-Drug Interaction (DDI)
o Drug-Target Interaction (DTI)
o Protein-Protein Interaction (PPI)
o Molecular Docking
o Biospecific and multitarget molecule design
o Virtual screening
o Innovative encapsulation and delivery
o Clinical trial analysis and design
o Intelligent drug experiment process design
o Reaction yield prediction
o Retrosynthesis pathway prediction
o Novel therapeutic use prediction
o Toxicity, bioactivity prediction
o Open drug database mining
o AI in nanomedicine
o Bioinformatics in drug design
o Knowledge management in drug design
