Artificial intelligence: Future aspects in the pharmaceutical industry an overview Bairagi Aakash*, Singhai Akhlesh K., Jain Ashish School of Pharmacy, LNCT University, Bhopal, Madhya Pradesh, India *Corresponding Author E-mail: aakashbairagi66@gmail.com
Online published on 10 April, 2025. Abstract Artificial intelligence (AI) has emerged as a potent tool leveraging human-like knowledge to offer swift solutions to intricate challenges. Striking advancements in AI technology and machine learning present a revolutionary opportunity in pharmaceutical drug discovery, formulation, and dosage form testing. By employing AI algorithms that scrutinize vast biological datasets encompassing genomics and proteomics, scientists can pinpoint disease-related targets and forecast their interactions with potential drug candidates. This facilitates a more precise and efficient approach to drug discovery, thereby elevating the chances of successful drug approvals. Moreover, AI holds the potential to curtail development costs by streamlining research and development processes. Machine learning algorithms aid in experimental design and can foresee the pharmacokinetics and toxicity of drug candidates, allowing for the prioritization and refinement of lead compounds, thereby reducing the necessity for extensive and expensive animal testing. Personalized medicine initiatives can be advanced through AI algorithms analyzing real-world patient data, culminating in more efficacious treatment outcomes and enhanced patient compliance. This comprehensive overview delves into the diverse applications of AI in pharmaceutical drug discovery, dosage form design for drug delivery, process refinement, testing, and pharmacokinetics/pharmacodynamics (PK/PD) investigations. It provides a glimpse into various AI-driven methodologies employed in pharmaceutical technology, shedding light on their advantages and limitations. Nonetheless, sustained investments in and exploration of AI within the pharmaceutical sector present promising avenues for enhancing drug development processes and patient care. Top Keywords Artificial intelligence (AI), Machine learning, Drug discovery, Formulation, Dosage form testing, Pharmacokinetics, Pharmacodynamics, PBPK, QSAR. Top |