Frontiers in Pharmacology has organized a series of special edition Research Topics with the goal of highlighting the achievements made by scientists researching hot topics. This collection aims to focus on the advancement of artificial intelligence and machine learning in Experimental Pharmacology and Drug Discovery.
Artificial intelligence (AI) has been used in drug discovery for the best part of the last decade. AI has resulted in a huge growth in drugs in clinical trials and on the market. The technology can address many challenges and constraints of traditional methods. AI has been used to map novel disease pathways, predict protein/drug interactions, and identify new targets and leads by analysis of phenotypic results as well as -omics data. AI can be used to predict small-molecule structure and activity, select drug leads based on pharmacokinetic and pharmacodynamic properties, and it can also be used to pre-emptively flag off-target effects. The potential of AI in drug discovery continues to grow.
The goal of this special edition Research Topic hosted in Experimental Pharmacology and Drug Discovery is to shed light on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives of Artificial Intelligence Experimental Pharmacology and Drug Discovery. This article collection hopes to inspire, inform and provide direction and guidance to researchers within the field.
We welcome original research and review articles on, but not limited to, the following topics:
• AI and big data in target identification and drug repurposing (through the mining of -omics/phenotypic/experimental/literature data).
• The use of AI to map novel disease pathways
• Prediction of protein/drug interactions, functions and drugability as well as prediction of novel binding sites and protein interactions using AI
• Modelling of changes in disease progression under pharmacotherapy
• Machine learning in population pharmacokinetic modelling
• AI-augmented exploration of the conformational space and/or binding sites of the partly disordered proteins for use in drug discovery
• Virtual screening to test drug candidates
• AI-driven de novo design of small molecules (e.g., molecular glues or another)/drugs
• Clinical significance of the codon (or in general DNA, e.g., epigenetic) mutations in (a given) cancer.
Please note: Manuscripts that only have in silico results will not be considered for review.
Frontiers in Pharmacology has organized a series of special edition Research Topics with the goal of highlighting the achievements made by scientists researching hot topics. This collection aims to focus on the advancement of artificial intelligence and machine learning in Experimental Pharmacology and Drug Discovery.
Artificial intelligence (AI) has been used in drug discovery for the best part of the last decade. AI has resulted in a huge growth in drugs in clinical trials and on the market. The technology can address many challenges and constraints of traditional methods. AI has been used to map novel disease pathways, predict protein/drug interactions, and identify new targets and leads by analysis of phenotypic results as well as -omics data. AI can be used to predict small-molecule structure and activity, select drug leads based on pharmacokinetic and pharmacodynamic properties, and it can also be used to pre-emptively flag off-target effects. The potential of AI in drug discovery continues to grow.
The goal of this special edition Research Topic hosted in Experimental Pharmacology and Drug Discovery is to shed light on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives of Artificial Intelligence Experimental Pharmacology and Drug Discovery. This article collection hopes to inspire, inform and provide direction and guidance to researchers within the field.
We welcome original research and review articles on, but not limited to, the following topics:
• AI and big data in target identification and drug repurposing (through the mining of -omics/phenotypic/experimental/literature data).
• The use of AI to map novel disease pathways
• Prediction of protein/drug interactions, functions and drugability as well as prediction of novel binding sites and protein interactions using AI
• Modelling of changes in disease progression under pharmacotherapy
• Machine learning in population pharmacokinetic modelling
• AI-augmented exploration of the conformational space and/or binding sites of the partly disordered proteins for use in drug discovery
• Virtual screening to test drug candidates
• AI-driven de novo design of small molecules (e.g., molecular glues or another)/drugs
• Clinical significance of the codon (or in general DNA, e.g., epigenetic) mutations in (a given) cancer.
Please note: Manuscripts that only have in silico results will not be considered for review.
