Advanced In Silico Methods and Digital Platforms for the Prediction of ADMET and Pharmacokinetics Properties
A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".
Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1878
Special Issue Editors
Interests: artificial intelligence; machine learning; cheminformatics; molecular docking; molecular dynamics; 3D-pharmacophore modeling; drug repurposing
Special Issues, Collections and Topics in MDPI journals
Interests: peptide–protein interactions; protein–protein interactions; drug repurposing; binding site mapping; machine learning; classification models; molecular docking; predictive toxicology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, the lack of efficacy and safety are the two major causes leading to drug failure. In the era of rapid technological progresses, there is a growing demand for innovative approaches to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) as well as pharmacokinetic (PK) properties of chemicals, having key roles in the evaluation of drug-likeness of compounds. These methods encompass a range of computational techniques, including quantitative structure–activity relationship (QSAR) modeling, physiologically based pharmacokinetic (PBPK) modeling, and molecular dynamics simulations. In this scenario, this Special Issue aims to explore the advancements of computer-aided techniques and technological platforms as emerging powerful tools that are able to accelerate the identification of promising drug candidates by minimizing the risks associated with late-stage failures.
In the light of this, we invite interested researchers to provide practical answers to challenging issues related to drug repurposing, de novo design, structure-based virtual screening, machine learning, artificial intelligence, molecular docking, and dynamics just to name a few, sharing their experiences on the design of novel methods and tools as well as the implementation of technological platforms.
Dr. Nicola Gambacorta
Dr. Daniela Trisciuzzi
Guest Editors
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Keywords
- ADMET properties prediction
- physiologically based pharmacokinetic (PBPK) modeling
- in silico digital platform
- chemoinformatics
- computer-aided drug discovery
- predictive toxicology
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