Applications of Artificial Intelligence and Machine Learning in Pharmaceutics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1693

Special Issue Editor

Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College,30-688 Kraków, Poland
Interests: machine learning; artificial intelligence; pharmaceutical technology; biopharmaceutics; clinical trials; statistics
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Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Applications of Artificial Intelligence in Pharmaceutics”, I would like to introduce a new one focused on modern computational approaches used for modeling in pharmaceutical sciences. Nowadays, machine learning (ML) has taken over the world of artificial intelligence (AI) as we deal with gradually increasing complexity in our analyses. Systems such as artificial neural networks, decision trees, fuzzy logic or evolutionary computations are omnipresent in various areas of science and technology.  Not surprisingly, AI/ML tools are also becoming more popular in the pharmaceutical sciences. The range of applications is vast and constantly growing, and it is noteworthy that industrial applications are exploding, especially in the area of big data. This is an especially hot topic in the context of drug discovery, process analytical technologies (PAT) and quality-by-design (QbD) systems, which are today obligatory in the pharmaceutical industry. AI/ML is supposed to transform pharmaceutical manufacturing towards Industry 4.0. In 2018, the FDA announced the Model-Informed Drug Development (MIDD) pilot program, which emphasizes the importance of advanced modeling approaches, with AI and AI/ML applications as a promising perspective. Aside from ML data-driven approaches, classical AI systems are still alive, so to speak. Even though expert systems are not easily developed, they remain applicable in the domain of pharmaceutical sciences.

Therefore, we call for publications covering every angle of either pure AI or AI/ML applications in the pharmaceutical sciences. From an industrial perspective, submission could cover various phases of the product lifecycle: from early discovery, through preclinical and clinical development, to the manufacturing and postmarketing phase. On a purely scientific ground, there might be pharmaceutical applications involving predictive modeling (regression and classification),  exploratory analyses and data mining, pattern recognition, and any other possible data science approaches to pharmaceutical science, e.g., natural language processing (NLP) for automated data harvesting from the pharmaceutical literature. We hope to attract a variety of visions to conceive a fruitful scientific discussion of how can we use AI/ML in the field of pharmaceutical sciences. We look forward to your submissions, and to a large body of exciting publications!

Dr. Aleksander Mendyk
Guest Editor

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Published Papers (1 paper)

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Research

9 pages, 1867 KiB  
Article
Network Pharmacology-Based Study on the Efficacy and Mechanism of Lonicera japonica Thunberg
by Sang Jun Park, Mi Hye Kim and Woong Mo Yang
Appl. Sci. 2022, 12(18), 9122; https://doi.org/10.3390/app12189122 - 11 Sep 2022
Cited by 1 | Viewed by 1276
Abstract
Network pharmacology is an emerging method for investigating the potential effects and mechanisms of natural products through system-level analyses of gene sets in herbs. Lonicera japonica Thunberg (LJ) is known to have anti-inflammatory, anti-bacterial, anti-oxidant, anti-tumor and neuroprotective effects. In the present study, [...] Read more.
Network pharmacology is an emerging method for investigating the potential effects and mechanisms of natural products through system-level analyses of gene sets in herbs. Lonicera japonica Thunberg (LJ) is known to have anti-inflammatory, anti-bacterial, anti-oxidant, anti-tumor and neuroprotective effects. In the present study, network pharmacological analysis was performed to assess the potential efficacy and mechanisms of LJ. First of all, constituents of LJ were gathered from public databases: the Oriental Advanced Searching Integrated System (OASIS) database, PubChem and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Then, a network was constructed using Cytoscape3.8.2, which visualizes biomedical interactions, and a functional enrichment analysis was conducted to uncover the pathways most relevant to LJ through Enrichr based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway 2021. Further, we performed a study of the literature to determine whether the results of our study were consistent with those of previous studies. The results showed that ‘Advanced glycation end products-Receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications’ was the pathway most relevant to LJ, especially through ‘Mitogen-activated protein kinase (MAPK) signaling pathway’, ‘Phosphatidylinositol 3 kinase-Protein kinase B (PI3K-AKT) signaling pathway’ and ‘Janus kinase-Signal transducers and activators of transcription (JAK-STAT) signaling pathway’. Based on the literature study, LJ showed relevance to MAPK, PI3K-AKT and JAK-STAT and was associated with therapeutic effects on diabetes and diabetic complications. This study shows that network pharmacology can be a suitable approach for analyzing LJ and suggests the potential efficacy and mechanisms of LJ. Full article
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