Aug 2026· Oriental Journal of Chemistry· 1 citation· 11 references
TL;DR
This paper examines and views the new innovations in combining the use of AI into harmonized medication design and targeted delivery systems, and 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.
Abstract
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.
An overview of the applications of AI in drug delivery is highlighted, focusing on AI-designed drug formulations, AI-driven prediction of ADMET properties, and AI-assisted drug delivery devices, which support the development of precision medicine.
Xinmin Yu, Xinyun Jiang, Tao Sheng et al.· ACS Nano· 1 citation
It is concluded that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve scientific decision-making within iterative lead optimization workflows and ultimately enhance translational success in drug discovery.
F. A. Mohamed, A. M. K. El-sagheir· Medicinal Chemistry Research· 0 citations
It is argued that clinical value will depend on closed-loop workflows in which multimodal predictions are experimentally validated, externally tested, and longitudinally updated to guide the next therapeutic decision.
K. Papavassiliou, A. Sofianidi, Angeliki Margoni et al.· International Journal of Mol...· 0 citations
Artificial Intelligence (AI) has become a fundamental driver of scientific progress, particularly in disease diagnosis, drug development, and drug delivery optimization. The intersection of AI, drug design, and nanosystems for delivery is accelerating the advancement of personalized nanomedicines and innovative dianano...
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 pr...
Neha Arora, Yogesh Matta, Monu Kumar et al.· Journal of Pharmaceutical Re...· 0 citations
A structured view of current advances, persistent gaps, and future directions in AI-enabled pharmaceutical innovation is offered, showing growing convergence between AI and precision therapeutics, but real-world application demands robust validation, collaboration, explainable AI, and global standards.
M. Fareed, S. Shityakov· Quantum Machine Intelligence· 0 citations
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