About this Research Topic
In the contemporary landscape of pharmacological research and healthcare, the integration of machine learning (ML) represents a transformative paradigm shift. The rapid evolution of ML techniques, driven by advances in computational power and data availability, has ushered in a new era in drug discovery and healthcare management. This Research Topic examines the multifaceted applications of ML in pharmacology, shedding light on its pivotal role in optimizing drug development processes, predicting drug interactions and adverse effects, personalizing treatment regimens, and elucidating intricate biological mechanisms. We aim to offer a comprehensive overview of the innovative solutions and transformative potential that this interdisciplinary convergence brings to the forefront.
By addressing recent breakthroughs, challenges, and cutting-edge best practices in the field of ML in pharmacology, this Research Topic aims to provide a comprehensive resource for researchers, clinicians, and industry professionals. It seeks to foster a deeper understanding of the transformative impact of ML on drug discovery, development, and personalized medicine. We hope to facilitate knowledge exchange, promote collaboration, and guide the implementation of innovative ML approaches, ultimately contributing to the advancement of pharmacological research and the delivery of more effective and tailored therapeutic interventions.
We will consider manuscripts including but not limited to the following subtopics:
Application of machine learning in drug discovery
Prediction of drug-target interactions
Modeling drug absorption, distribution, metabolism, and excretion (ADME)
Personalized medicine through machine learning
Adverse event prediction and monitoring
Precision oncology and machine learning
Pharmacogenomics for personalized drug prescriptions
Biomedical text mining for drug discovery insights
Clinical trial optimization using machine learning
Ethical and regulatory considerations in machine learning for pharmacology
We welcome a variety of article types, including under the following categories: Original Research, Review, Mini Review, Brief Research Report, and Perspective.
By addressing recent breakthroughs, challenges, and cutting-edge best practices in the field of ML in pharmacology, this Research Topic aims to provide a comprehensive resource for researchers, clinicians, and industry professionals. It seeks to foster a deeper understanding of the transformative impact of ML on drug discovery, development, and personalized medicine. We hope to facilitate knowledge exchange, promote collaboration, and guide the implementation of innovative ML approaches, ultimately contributing to the advancement of pharmacological research and the delivery of more effective and tailored therapeutic interventions.
We will consider manuscripts including but not limited to the following subtopics:
Application of machine learning in drug discovery
Prediction of drug-target interactions
Modeling drug absorption, distribution, metabolism, and excretion (ADME)
Personalized medicine through machine learning
Adverse event prediction and monitoring
Precision oncology and machine learning
Pharmacogenomics for personalized drug prescriptions
Biomedical text mining for drug discovery insights
Clinical trial optimization using machine learning
Ethical and regulatory considerations in machine learning for pharmacology
We welcome a variety of article types, including under the following categories: Original Research, Review, Mini Review, Brief Research Report, and Perspective.
Keywords: Machine Learning in Pharmacology, Drug Discovery Optimization, Biomedical Text Mining, Personalized Medicine, Drug Interactions, AI, Artificial Intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
