Artificial Intelligence and Machine Learning in Drug Discovery and Pharmacy Practice: An Indian Perspective
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) and machine learning (ML) are increasingly being incorporated into pharmaceutical research, drug discovery and pharmacy practice. Conventional drug development is constrained by complex biology, large experimental workloads, lengthy development pathways, high expenditure and substantial attrition between discovery and clinical approval. AI/ML methods can analyse chemical, biological, clinical and literature-derived data to support target identification, virtual screening, molecular property prediction, de novo molecular design, lead optimisation, ADMET prediction, drug repurposing and clinical-development activities. In pharmacy practice, AI can support medication review, drug–drug interaction detection, medication-error identification, pharmacovigilance, clinical decision support, medication reconciliation and patient-facing digital services. Recent evidence also indicates a rapidly expanding role for AI in clinical pharmacy workflows, although most applications remain decision-support tools rather than autonomous clinical systems. [1–6] The Indian context is especially important because India combines a major pharmaceutical manufacturing base with expanding biomedical research, digital-health infrastructure and government-supported pharmaceutical R&D. The Department of Pharmaceuticals has established the PRIP scheme to promote research and innovation in the Pharma MedTech sector, while the Indian Council of Medical Research (ICMR) has issued dedicated ethical guidance for AI in biomedical research and healthcare. A recent survey of Indian pharmacologists found that 68.26% of respondents were familiar with AI/ML/deep learning, while major concerns included algorithmic bias, skill loss, job displacement and data security. [7–10] This review critically examines the opportunities, evidence, limitations, ethical considerations, regulatory context and future directions for responsible AI adoption in Indian pharmacy and pharmaceutical science.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225302237