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: 31 May 2025 | Viewed by 345

Special Issue Editor

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

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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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Published Papers (1 paper)

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17 pages, 1522 KiB  
Perspective
From Patterns to Pills: How Informatics Is Shaping Medicinal Chemistry
by Alexander Trachtenberg and Barak Akabayov
Pharmaceutics 2025, 17(5), 612; https://doi.org/10.3390/pharmaceutics17050612 - 5 May 2025
Viewed by 149
Abstract
In today’s information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance [...] Read more.
In today’s information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance of informatics in shaping the field of medicinal chemistry, particularly through the concept of the “informacophore”. The informacophore refers to the minimal chemical structure, combined with computed molecular descriptors, fingerprints, and machine-learned representations of its structure, that are essential for a molecule to exhibit biological activity. Similar to a skeleton key unlocking multiple locks, the informacophore points to the molecular features that trigger biological responses. By identifying and optimizing informacophores through in-depth analysis of ultra-large datasets of potential lead compounds and automating standard parts in the development process, there will be a significant reduction in biased intuitive decisions, which may lead to systemic errors and a parallel acceleration of drug discovery processes. Full article
(This article belongs to the Special Issue Advancements in AI and Pharmacokinetics)
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