Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing

Abstract

Yedi Herdiana,1 Ahmed Fouad Abdelwahab Mohammed,2,3 Angga Prawira Kautsar,1 Iyan Sopyan,1 Nasrul Wathoni1 1Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia; 2Department of Pharmaceutics, Faculty of Pharmacy, Minia University, Minia, Egypt; 3Department of Pharmaceutics, Faculty of Pharmacy, Minia National University, New Minia, EgyptCorrespondence: Yedi Herdiana, Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia, Email y.herdiana@unpad.ac.idAbstract: Artificial Intelligence (AI) is increasingly being implemented in pharmaceutical sciences and has the potential to improve efficiency across the value chain, from drug candidate discovery to manufacturing, quality monitoring, and regulatory process support. Nonetheless, the integration of AI within the pharmaceutical sector encounters persistent obstacles, such as data interoperability and fragmentation, the necessity for model validation and governance to satisfy compliance standards, the potential for bias and accountability concerns, and deficiencies in workforce skills. This review consolidates significant advancements in AI applications, such as generative AI, laboratory automation, and the digital twin concept, highlighting that effective implementation relies on workflow integration, data quality and integrity, and sufficient human-in-the-loop mechanisms. We propose strategic recommendations centred on human resource readiness, governance structures, and technology maturity assessment to assist readers in differentiating feasible solutions from aspirational frameworks. Moving forward, research and adoption will likely highlight precision medicine and regulatory–industry collaboration mechanisms for AI evaluation. The integration of AI with supporting technologies such as tamper-evident provenance/audit layers (such as blockchain) remains exploratory and generally limited to pilots.Keywords: generative ai in drug discovery, self-driving laboratories, closed-loop discovery, pharma 4.0, digital twins in healthcare

Ähnliche Arbeiten