Aug 2026· Journal of Pharmaceutical Research and Integrated Medical Sciences· Vol 3, pp. 01-14· 0 citations
TL;DR
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.
Abstract
Artificial intelligence (AI) has emerged as one of the most transformative technologies in pharmaceutical research by accelerating drug discovery and medicinal chemistry through machine learning, deep learning, and advanced computational approaches. 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. The review also explores interdisciplinary approaches integrating medicinal chemistry, bioinformatics, structural biology, cheminformatics, and digital healthcare to improve molecular design, reduce research costs, and enhance drug development efficiency. Current evidence indicates that AI significantly improves the accuracy and speed of discovering novel therapeutic compounds while supporting personalized treatment strategies and optimizing clinical trials across diverse therapeutic areas. Despite these advancements, challenges remain regarding data quality, model interpretability, algorithmic bias, regulatory acceptance, and prospective clinical validation. Addressing these limitations through interdisciplinary collaboration and standardized validation frameworks will further strengthen the role of artificial intelligence in advancing drug discovery and medicinal chemistry.
Artificial intelligence (AI) has emerged as a major technological development influencing modern
pharmaceutical research and development. Machine learning, computational biology, and large-scale
biological data analysis have the potential to improve target identification, molecular optimization, and
clinical development. However, despite growing enthusiasm, significant uncertainty remains regarding
the ability of AI systems to overcome the biological, regulatory, and translational challenges that have
historically limited pharmaceutical innovation. This review examines AI-driven drug discovery from
a biomedical engineering and translational perspective. It evaluates the scientific foundations of AIenabled
pharmaceutical development, including target identification, molecular design, and multimodal
biological modeling, while analyzing key barriers involving biological complexity, clinical translation,
regulatory oversight, and commercialization. Case studies of Recursion Pharmaceuticals and Schrödinger
demonstrate both the opportunities and limitations associated with integrating computational approaches
into therapeutic development. 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. AI should therefore be viewed as an enabling
technology that enhances decision-making and prioritization rather than a replacement for traditional
biomedical research processes.
Andrew Matelis· American Journal of Student...· 0 citations
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
Mohsen Zabihi· Advances in Pharmacology and...· 0 citations
This review examines how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development, and highlights how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design.
Kaicheng U, Sophia Meixuan Zhang, Ziyu Yu et al.· Chemical Society Reviews· 0 citations
Artificial intelligence (AI) is increasingly reshaping the biomedical continuum, offering new capabilities from early discovery to patient care. This review explores current advances, starting with drug discovery, where AI aids in target identification, virtual screening, lead optimization, and prediction of pharmacokinetic and toxicological profiles, thereby reducing development costs and attrition rates. In preclinical and translational research, AI may support improvements in in vitro–in vivo correlations, physiologically based pharmacokinetic modeling, and the development of AI-assisted approaches that could complement selected animal studies. These applications may improve predictive reliability while also helping to address some ethical concerns related to preclinical evaluation. In clinical trials, AI may have a role in patient recruitment, adaptive trial design, dose prediction, and the analysis of real-world evidence. In clinical medicine and pharmacy practice, its use is also increasing across diagnostic support, clinical decision-making, pharmacovigilance, and supply chain management. These applications could improve the delivery of more personalized and accessible care, but this is not always straightforward, since the usefulness of AI depends on the quality of the available data, the validation process, and how the system is applied in real clinical settings. Several concerns, therefore, remain, particularly regarding transparency, algorithmic bias, data privacy, validation, and regulatory compliance. The review also discusses possible future directions, including precision medicine, digital twin technologies, and the integration of AI with emerging biotechnologies, while stressing the need for responsible, validated, and equitable use of AI in healthcare.
Tha’er Ata, I. Al-Ani, Derar H. Abdel-Qader et al.· Artificial Intelligence in H...· 0 citations
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.
Junius Gian Ginting· Formosa Journal of Multidisc...· 0 citations
This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.
Mohsin Ali, Muhammad Ali Tajwar, Farid Ahmed et al.· Medicinal research reviews (...· 0 citations