1Department of Pharmaceutical Science, Siddhivinayak College of Pharmacy, Warora, 442914, Chandrapur, Maharashtra, India.
2Department of Pharmaceutical Science, Institute of Pharmaceutical Education and Research, Borgaon (Meghe)Wardha,442001, Maharashtra, India
Artificial intelligence (AI) is rapidly transforming pharmaceutical sciences by providing data?driven solutions for drug discovery, formulation development, clinical research, and pharmacovigilance. AI?based systems streamline molecular screening, predict pharmacokinetic behavior, optimize formulation variables, and enhance patient safety monitoring. The integration of machine learning, deep learning, and computational modeling has significantly reduced time, cost, and experimental failures in drug development. This review summarizes current trends, major applications, and future prospects of AI in pharmaceutical sciences while addressing ethical limitations, data challenges, and regulatory considerations.
The integration of AI into pharmaceutical sciences is transforming traditional research and development processes. AI systems analyze vast datasets, predict molecular behavior, optimize therapeutic strategies, and support data?driven decision?making. The pharmaceutical industry faces increasing demand for speed, accuracy, and cost?effectiveness in drug development. AI addresses these needs by minimizing experimental failures, reducing development time, and improving prediction accuracy. Numerous industries are striving to enhance their progress to meet the demands and expectations of their customers, utilizing various methodologies. The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases. The assessment of the significant levels of toxicity associated with new drugs is an area of considerable concern, necessitating extensive research and exploration in the foreseeable future. One of the primary aims is to provide drug molecules that offer optimal benefits and suitability for utilization in the healthcare industry. Despite this, the pharmacy industry faces numerous obstacles that necessitate further advancement using technology-driven methods to address worldwide medical and healthcare demands [3,4,5]. The need for a proficient workforce in the healthcare industry is persistent, necessitating the continuous provision of training to healthcare personnel to augment their involvement in routine duties. Identifying skill gaps in the workplace is a crucial undertaking within the pharmaceutical industry. It is imperative to effectively address the identified gaps through appropriate remedial measures while acknowledging that providing adequate training can also pose a significant challenge. As per a report presented by certain authorities, it has been observed that approximately 41% of supply chain disruptions occurred in June 2022. The report further highlights that supply chain disruption has emerged as the second-most-formidable challenge to overcome. Several pharmaceutical industries are anticipating further advancements in their supply chain, as well as innovative models to address these challenges, with the potential to enhance business resilience [6]. The global outbreak of coronavirus disease 2019 (COVID-19) has caused significant disruptions to various operations worldwide, including ongoing clinical trials [7].
SCOPE AND OBJECTIVES OF THE REVIEW:
This review encompasses AI applications across four key pillars of pharmaceutical innovation, each addressing critical challenges in the drug development lifecycle.
Ruswa Urade*, Sujata Samant, Sandip Umare, Rupal Kalbhut, Bhudevi Khapne, An Artificial Intelligence in Pharmaceutical Sciences: Current Trends, Applications, and Future Prospects, Int. J. Sci. R. Tech., 2025, 2 (11), 623-630. https://doi.org/10.5281/zenodo.17668758
10.5281/zenodo.17668758
