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 advancements in computing 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, highlighting its central role in optimizing drug development processes, predicting drug interactions and adverse effects, personalizing treatment regimens, and elucidating complex biological mechanisms. Our goal is to offer a comprehensive overview of the innovative solutions and transformative potential that this interdisciplinary convergence brings to the forefront.
By addressing recent advances, 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, thereby contributing to the advancement of pharmacological research and the implementation of more effective and better tailored therapeutic interventions. .
We will consider manuscripts including, but not limited to, the following subtopics:
Applying machine learning to drug discovery
Prediction of drug-target interactions
Modeling drug absorption, distribution, metabolism and excretion (ADME)
Personalized medicine through machine learning
Prediction and monitoring of adverse events
Precision oncology and machine learning
Pharmacogenomics for personalized drug prescriptions
Mining biomedical texts for insights into drug discovery
Optimizing clinical trials using machine learning
Ethical and Regulatory Considerations in Machine Learning for Pharmacology
We welcome a variety of article types, including 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 fall 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 appropriate section or journal at any stage of peer review.
