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Pharmaceutics
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Computational Intelligence (CI) Tools in Applications of Pharmaceutics
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Computational Intelligence (CI) Tools in Applications of Pharmaceutics

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmaceutical Technology, Manufacturing and Devices".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 9921

Special Issue Editors

Dr. Jakub Szlęk

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Guest Editor
Chair and Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College, Medyczna 9 St, 30-688 Kraków, Poland
Interests: pharmaceutical technology; machine learning; solid dosage forms; drug dissolution; biopharmaceutics
Special Issues, Collections and Topics in MDPI journals
Dr. Adam Pacławski

E-Mail Website
Guest Editor
Chair and Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College, Medyczna 9 St, 30-688 Kraków, Poland
Interests: artificial intelligence; machine learning; pulmonary drug delivery; particle technology; spray drying; biopharmaceutics; image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second edition of a previous Special Issue: Computational Intelligence (CI) Tools in Drug Discovery and Design.

https://www.mdpi.com/journal/pharmaceutics/special_issues/CI_drug_design

The demand for new drugs has increased in recent decades. Therefore, the discovery and development of new drugs and their pharmaceutical forms should be fast and efficient, while maintaining high quality. This may require the use of computational intelligence (CI) tools. CI usually refers to a program that is able to solve complex problems without any prior knowledge of a phenomenon, by learning from data or experimental observations. Computers currently surpass the human brain in terms of data processing, and, if properly designed, computer programs could significantly accelerate the development of new drugs. Moreover, CI tools could help to discover complex and sometimes unobvious interactions between drugs and biological targets.

This Special Issue of Pharmaceutics seeks to gather novel and interesting scientific research findings regarding the application of computational intelligence tools in drug discovery and development. The focus will be on research articles and reviews on drug dosage forms and novel substances whose development is motivated by computational intelligence tools. Studies on other technological and pharmaceutical aspects of computer-aided drug design will also be welcome.

Dr. Jakub Szlęk
Dr. Adam Pacławski
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 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

  • machine learning in drug design and development artificial intelligence
  • data science
  • heuristic modeling of pharmaceutical processes
  • QSPR models

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Published Papers (4 papers)

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Research

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14 pages, 2103 KiB  
Article
Initial Development of Automated Machine Learning-Assisted Prediction Tools for Aryl Hydrocarbon Receptor Activators
by Paulina Anna Wojtyło, Natalia Łapińska, Lucia Bellagamba, Emidio Camaioni, Aleksander Mendyk and Stefano Giovagnoli
Pharmaceutics 2024, 16(11), 1456; https://doi.org/10.3390/pharmaceutics16111456 - 15 Nov 2024
Viewed by 327
Abstract
Background: The aryl hydrocarbon receptor (AhR) plays a crucial role in immune and metabolic processes. The large molecular diversity of ligands capable of activating AhR makes it impossible to determine the structural features useful for the design of new potent modulators. Thus, [...] Read more.
Background: The aryl hydrocarbon receptor (AhR) plays a crucial role in immune and metabolic processes. The large molecular diversity of ligands capable of activating AhR makes it impossible to determine the structural features useful for the design of new potent modulators. Thus, in the field of drug discovery, the intricate nature of AhR activation necessitates the development of novel tools to address related challenges. Methods: In this study, quantitative structure–activity relationship (QSAR) models of classification and regression were developed with the objective of identifying the most effective method for predicting AhR activity. The initial dataset was obtained by combining the ChEMBL and WIPO databases which contained 978 molecules with EC50 values. The predictive models were developed using the automated machine learning platform mljar according to a 10-fold cross validation (10-CV) testing procedure. Results: The classification model demonstrated an accuracy value of 0.760 and F1 value of 0.789 for the test set. The root-mean-squared error (RMSE) was 5444, and the coefficient of determination (R2) was 0.208 for the regression model. The Shapley Additive Explanations (SHAP) method was then employed for a deeper comprehension of the impact of the variables on the model’s predictions. As a practical application for scientific purposes, the best performing classification model was then used to develop an AhR web application. This application is accessible online and has been implemented in Streamlit. Conclusions: The findings may serve as a foundation in prompting further research into the development of a QSAR model, which could enhance comprehension of the influence of ligand structure on the modulation of AhR activity. Full article
(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Applications of Pharmaceutics)
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16 pages, 3296 KiB  
Article
Integrated QSAR Models for Prediction of Serotonergic Activity: Machine Learning Unveiling Activity and Selectivity Patterns of Molecular Descriptors
by Natalia Łapińska, Adam Pacławski, Jakub Szlęk and Aleksander Mendyk
Pharmaceutics 2024, 16(3), 349; https://doi.org/10.3390/pharmaceutics16030349 - 1 Mar 2024
Cited by 1 | Viewed by 2004
Abstract
Understanding the features of compounds that determine their high serotonergic activity and selectivity for specific receptor subtypes represents a pivotal challenge in drug discovery, directly impacting the ability to minimize adverse events while maximizing therapeutic efficacy. Up to now, this process has been [...] Read more.
Understanding the features of compounds that determine their high serotonergic activity and selectivity for specific receptor subtypes represents a pivotal challenge in drug discovery, directly impacting the ability to minimize adverse events while maximizing therapeutic efficacy. Up to now, this process has been a puzzle and limited to a few serotonergic targets. One approach represented in the literature focuses on receptor structure whereas in this study, we followed another strategy by creating AI-based models capable of predicting serotonergic activity and selectivity based on ligands’ representation by molecular descriptors. Predictive models were developed using Automated Machine Learning provided by Mljar and later analyzed through the SHAP importance analysis, which allowed us to clarify the relationship between descriptors and the effect on activity and what features determine selective affinity for serotonin receptors. Through the experiments, it was possible to highlight the most important features of ligands based on highly efficient models. These features are discussed in this manuscript. The models are available in the additional modules of the SerotoninAI application called “Serotonergic activity” and “Selectivity”. Full article
(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Applications of Pharmaceutics)
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Review

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27 pages, 2107 KiB  
Review
Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine
by Dolores R. Serrano, Francis C. Luciano, Brayan J. Anaya, Baris Ongoren, Aytug Kara, Gracia Molina, Bianca I. Ramirez, Sergio A. Sánchez-Guirales, Jesus A. Simon, Greta Tomietto, Chrysi Rapti, Helga K. Ruiz, Satyavati Rawat, Dinesh Kumar and Aikaterini Lalatsa
Pharmaceutics 2024, 16(10), 1328; https://doi.org/10.3390/pharmaceutics16101328 - 14 Oct 2024
Viewed by 5838
Abstract
Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the [...] Read more.
Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare. Full article
(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Applications of Pharmaceutics)
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68 pages, 10858 KiB  
Review
Leveraging Numerical Simulation Technology to Advance Drug Preparation: A Comprehensive Review of Application Scenarios and Cases
by Qifei Gu, Huichao Wu, Xue Sui, Xiaodan Zhang, Yongchao Liu, Wei Feng, Rui Zhou and Shouying Du
Pharmaceutics 2024, 16(10), 1304; https://doi.org/10.3390/pharmaceutics16101304 - 7 Oct 2024
Viewed by 1209
Abstract
Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. [...] Read more.
Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. Methods: In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. Results: By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. Conclusions: The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models. Full article
(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Applications of Pharmaceutics)
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