Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
10.0
5.5
Journals
Pharmaceutics
Special Issues
Advanced In Silico Methods and Digital Platforms for the Prediction...
share announcement

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 (31 October 2025) | Viewed by 7525

Special Issue Editors

Dr. Nicola Gambacorta

E-Mail Website
Guest Editor
Division of Medical Genetics, IRCSS Foundation “Casa Sollievo dalla Sofferenza” San Giovanni Rotondo, 71013 Foggia, Italy
Interests: artificial intelligence; machine learning; cheminformatics; molecular docking; molecular dynamics; 3D-pharmacophore modeling; drug repurposing
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Trisciuzzi

E-Mail Website
Guest Editor
Department of Pharmacy, Pharmaceutical Sciences Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy
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

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 250 words) can be sent to the Editorial Office for assessment.

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

  • ADMET properties prediction
  • physiologically based pharmacokinetic (PBPK) modeling
  • in silico digital platform
  • chemoinformatics
  • computer-aided drug discovery
  • predictive toxicology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 14381 KB  
Article
Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity
by Ilia Nikitin, Igor Morgunov, Victor Safronov, Anna Kalyuzhnaya and Maxim Fedorov
Pharmaceutics 2025, 17(12), 1573; https://doi.org/10.3390/pharmaceutics17121573 - 5 Dec 2025
Viewed by 479
Abstract
Objectives: One of the major trends in modern computational toxicology is the development of explainable predictive tools. However, the complex nature of the mechanistic representation of biological organisms and the lack of relevant data remain limiting factors. Methods: This work provides a publicly [...] Read more.
Objectives: One of the major trends in modern computational toxicology is the development of explainable predictive tools. However, the complex nature of the mechanistic representation of biological organisms and the lack of relevant data remain limiting factors. Methods: This work provides a publicly available dataset of 12,654 compounds with mouse intravenous LD50 values, as well as docking scores (Vina-GPU 2.0) against 44 toxicity-associated proteins. NIH and Brenk filters were applied to refine the chemical space. Results: Across the entire protein panel, the human ether-a-go-go–related gene channel (hERG/KCNH2), vasopressin receptor 1A (AVPR1A), the L-type voltage-gated calcium channel Cav1.2 (CACNA1C), the potassium voltage-gated channel subfamily KQT member 1 (KCNQ1) and endothelin receptor A (EDNRA) showed the strongest association with acute toxicity. Statistically significant differences were found in the distribution of LD50 values for compounds that bind antitargets compared with non-binders. Using known bioactive molecules such as anisodamine, butaperazine, soman, and several cannabinoids as examples confirmed the effectiveness of inverse docking for elucidating mechanism of action. Conclusions: The dataset offers a resource to advance transparent, mechanism-aware toxicity modeling. The data is openly available. Full article
(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for the Prediction of ADMET and Pharmacokinetics Properties)
►▼ Show Figures

Figure 1

13 pages, 2643 KB  
Article
Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data
by Pranav Shah, Vishal B. Siramshetty, Ewy Mathé and Xin Xu
Pharmaceutics 2024, 16(10), 1257; https://doi.org/10.3390/pharmaceutics16101257 - 27 Sep 2024
Cited by 2 | Viewed by 2618
Abstract
Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of [...] Read more.
Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. Methods: We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. Results: Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. Conclusions: The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data. Full article
(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for the Prediction of ADMET and Pharmacokinetics Properties)
►▼ Show Figures

Figure 1

Review

Jump to: Research

58 pages, 681 KB  
Review
In Silico ADME Methods Used in the Evaluation of Natural Products
by Robert Ancuceanu, Beatrice Elena Lascu, Doina Drăgănescu and Mihaela Dinu
Pharmaceutics 2025, 17(8), 1002; https://doi.org/10.3390/pharmaceutics17081002 - 31 Jul 2025
Cited by 2 | Viewed by 3780
Abstract
The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the [...] Read more.
The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article
(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for the Prediction of ADMET and Pharmacokinetics Properties)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop