Jul 2026· Formosa Journal of Multidisciplinary Research· Vol 5, pp. 1923-1938· 0 citations
TL;DR
The findings reveal that AI significantly accelerates drug discovery, supports personalized medicine through multi-omics data integration, enhances sustainability by improving resource efficiency, and drives pharmaceutical innovation through generative AI technologies.
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in the pharmaceutical industry, offering innovative solutions to accelerate drug discovery, enhance precision medicine, and improve sustainability in healthcare systems. Conventional drug development is often characterized by lengthy timelines, high costs, and significant failure rates, creating a need for more efficient and data-driven approaches. This study aims to analyze the role of AI in optimizing sustainable modern precision drug development. The research employed a Systematic Literature Review (SLR) method following the PRISMA 2020 framework. Literature was collected from major scientific databases, including Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, IEEE Xplore, and Google Scholar. From an initial pool of 152 articles, 10 studies published between 2020 and 2025 met the inclusion criteria and were analyzed using thematic synthesis. The findings reveal that AI significantly accelerates drug discovery, supports personalized medicine through multi-omics data integration, enhances sustainability by improving resource efficiency, and drives pharmaceutical innovation through generative AI technologies. However, challenges related to data quality, transparency, ethics, and regulatory compliance remain critical barriers. Overall, AI represents a strategic enabler for sustainable, efficient, and patient-centered pharmaceutical development.
This review examines the available literature from human clinical studies, computational drug discovery research, systematic reviews, meta-analyses, and clinical investigations, highlighting the applications of AI in target identification, virtual screening, lead optimization, drug repurposing, ADMET prediction, and precision medicine.
Neha Arora, Yogesh Matta, Monu Kumar et al.· Journal of Pharmaceutical Re...· 0 citations
The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.
Krishna Chandra Panda, B. R. Ravi Kumar, J. Sruti et al.· Reviews on recent clinical t...· 0 citations
Artificial-intelligence (AI) is transforming the pharmaceutical industry by simplifying data-driven innovations in drugs’ research and delivery systems. This paper examines and views the newinnovations in combining the use of AI into harmonized medication design and targeted delivery systems. AI-driven models speed-up molecular design, predict pharmacokinetic outcomes, and improve the nanocarrier preparations, reducing the costs and time associated to conventional experimental techniques. Furthermore, AI contentsmake use of computational chemistry and synthesis-based science, improving medicinal effectiveness, precision, and controlled release. It highlights ethical implications and the interaction between computational intelligence and chemical innovation as a means to advance next-generation intelligent therapies and precision medicine. Also, it analyzes AI-driven molecular design with synthetic level reports, emphasizing explainable AI, digital twin platforms, and translations that characterize the next generation of precision pharmaceuticals, distinguishing it from previous narrative reviews.
Pharmaceutical analysis is undergoing substantial transformation through artificial intelligence (AI), machine learning (ML), Process Analytical Technology (PAT), and sustainable analytical approaches. This review critically evaluates recent advances in AI-assisted pharmaceutical analysis, emphasizing chromatographic optimization, predictive stability assessment, impurity profiling, continuous manufacturing, and real-time monitoring systems. A structured literature survey was conducted using PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, and Google Scholar databases covering publications from 2020 to early 2026. The reviewed literature indicates that AI-assisted analytical workflows can significantly reduce method development time, improve predictive accuracy, support continuous process monitoring, and enhance analytical sustainability. Integration of AI with QbD and PAT has demonstrated potential benefits in pharmaceutical quality assurance and manufacturing efficiency. Despite substantial progress, limitations including insufficient validation datasets, algorithmic bias, model interpretability issues, and regulatory uncertainty continue to restrict widespread implementation. Future developments in explainable AI, regulatory harmonization, and robust validation frameworks are expected to facilitate broader industrial adoption.
Annasaheb S. Gaikwad, Sonali B. Pawar· Discover Artificial Intellig...· 0 citations
The analysis suggests that AI will become an increasingly important component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic advancement alone and more on effective integration with biological validation, experimental rigor, clinical evidence, and scalable translational infrastructure.
Andrew Matelis· American Journal of Student...· 0 citations
Ethnomedical knowledge has contributed to the discovery and development of several contemporary antidiabetic agents. However, the extensive global diversity of medicinal plants, phytochemicals, and traditional therapeutic systems creates a substantial challenge for systematic prioritization. This review was conducted to map the application of artificial intelligence (AI) and big data approaches to support evidence-based prioritization in antidiabetic ethnopharmacology.
A systematic mapping review was conducted and reported in accordance with PRISMA 2020. PubMed and Scopus were searched using predefined terms related to diabetes, medicinal plants or traditional medicine, and AI or big-data computational methods. Eligible studies were synthesized using descriptive statistics to characterize the distribution of AI methodologies, data sources, computational workflows, ethnopharmacological integration, and validation levels.
Twenty-nine studies met the inclusion criteria. Machine learning was the most frequently applied approach (25%), mainly for compound recognition, bioactivity prediction, and candidate prioritization. Many studies used dual-focus computational architectures, in which prediction-oriented AI methods, including machine learning or deep learning, were combined with mechanistic frameworks such as network pharmacology and molecular modeling (38%). Validation was mainly restricted to in silico analyses. Ethnopharmacological integration was commonly mediated by secondary database proxies, such as herb lists, indications, and compound–target annotations, rather than by structured modeling of primary ethnomedicinal practice.
AI and big-data methods are being used to reshape selected stages of antidiabetic ethnopharmacology, particularly candidate prioritization, target prediction, and network-based mechanism generation. However, most evidence remains hypothesis-generating.
R. M. Febriyanti, A. A. Irawan, Ami Tjitraresmi et al.· Discover Artificial Intellig...· 0 citations
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