AI-Driven Target Identification: Revolutionising Drug Discovery in India’s Biopharmaceutical Industry

Abstract

This study examined the impact of artificial intelligence (AI) on target identification in India's biopharmaceutical sector, specifically in the context of drug discovery. It looked at the degree of AI use, how it affects accuracy and efficiency, the main operational difficulties, how ready we are for new developments in AI, and how ethical and regulatory issues are currently being handled. A structured quantitative survey aimed at biopharma experts was used to gather data using a mixed-methods technique, which was bolstered by secondary analysis of industry changes. Results showed that although big companies like Biocon and Dr. Reddy's Laboratories are pushing AI integration, small and medium-sized businesses are still not adopting it at a high rate because of talent, infrastructure, and budgetary constraints. AI applications have been demonstrated to drastically cut down on discovery timeframes and increase target prediction accuracy in digitally mature pharmaceutical firms. However, problems including scattered digital infrastructure, a lack of qualified professionals, and poor data quality still exist. Additionally, the industry lacks ethical standards and legal frameworks tailored to AI, which leads to ambiguity and inconsistent application. The study indicates that while AI has the potential to revolutionise early-stage drug discovery in India, methodical efforts are required to expand workforce development, enhance infrastructure, and create clear governance norms. These results provide valuable insights for academic institutions, industry executives, and policymakers seeking to promote the ethical, efficient, and inclusive use of AI in pharmaceutical research in India. India's biopharmaceutical industry must embrace AI technologies and cultivate an innovative environment to stay competitive on a global scale. Scalability is hampered by the lack of standardised frameworks to assess AI performance throughout the drug discovery process. The need for flexible regulatory frameworks and funding for interdisciplinary education will only grow as artificial intelligence develops. Opportunities for more research on explainability, AI training, and responsible algorithm deployment are highlighted in this work. By filling these gaps, India can become a pioneer in morally sound, AI-powered pharmaceutical development.