Advancements in AI and Pharmacokinetics
A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Drug Targeting and Design".
Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 597
Special Issue Editor
Interests: drug design; therapy; OMICs; proteomics; data integration; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will explore advancements in artificial intelligence (AI) and pharmacokinetics within nanomedicine. The focus is on the integration of multi-OMIC data with AI for drug targeting and design. Submissions using computational models of biological experimental validation are welcome.
Topics include the following:
- AI algorithms improving precision and efficiency: Reports of how advanced AI algorithms and models are revolutionizing the precision and efficiency of drug targeting and design are welcome. This could include specific examples of AI-driven methodologies that have led to successful drug development.
- AI prediction and analyses of the toxicokinetics of new compounds: We invite submissions of investigations of how AI can predict and analyze the toxicokinetics of new drug compounds, including how the body absorbs, distributes, metabolizes, and excretes them. This could include a focus on the implications of these predictions for the safety and efficacy of drugs.
- Understanding AI and predicting pharmacokinetic and pharmacodynamic profiles: Discussions of how AI is enhancing the understanding and prediction of the pharmacokinetic (i.e., what the body does to a drug) and pharmacodynamic (i.e., what the drug does to the body) profiles of drugs are welcome. This could include examples of AI models that have successfully predicted drug behavior.
- AI-driven insights into the physiological and biochemical impacts of drugs: Authors are welcome to submit examinations of the role of AI in providing insights into the physiological and biochemical impacts of drugs on the body. We especially welcome studies in which AI has uncovered new information about drug interactions and side effects.
- AI modeling and understanding drug–receptor interactions: This Special Issue could include descriptions of how AI is being used to model and understand drug–receptor interactions at a molecular level. These might discuss the significance of these models in drug design and how they can lead to more effective treatments.
Dr. Raj Sewduth
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI in nanomedicine
- drug targeting
- pharmacokinetics
- pharmacodynamics
- drug–receptor interactions
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