Artificial Intelligence in Drug Discovery Market in India, 2024

In India's drug discovery field, AI utilizes advanced computational techniques to expedite various stages of drug development. AI is pivotal in deciphering intricate biological data, simplifying target identification, and enhancing lead compound searches. The incorporation of machine learning, deep learning, and natural language processing enables the analysis of vast datasets, facilitating predictive modelling for personalized medicine, drug interactions, and biomarker discovery.

Market insights:

The Indian market for artificial intelligence (AI) in drug discovery is projected to grow to INR 2.57 trillion by 2028, with a compound annual growth rate (CAGR) of 30.82% from 2023 to 2028. AI is poised to become a lucrative technology in healthcare, filling the research and development gap in drug manufacturing and enhancing targeted drug production. As a result, biopharmaceutical companies are increasingly embracing AI to bolster their market presence.

Market drivers:

The rising prevalence of chronic diseases, such as heart disease, diabetes, cancer, and neurological disorders, underscores the growing healthcare burden, necessitating prolonged treatment. Drug discovery plays a pivotal role in identifying effective therapies for these conditions, with AI, particularly leveraging machine learning and deep learning applications, meeting this demand by efficiently analyzing extensive datasets encompassing genomics, proteomics, and clinical data. As technologies evolve, especially in machine learning, deep learning, and data analytics, AI becomes increasingly transformative in pharmaceutical research and development, enabling the analysis of intricate biological datasets with unprecedented efficiency and accuracy.

Market challenges:

The stringent regulations and guidelines present a challenge, hindering the seamless integration of AI into India's drug discovery market. While vital for patient safety and data integrity, the complexity of regulatory frameworks may not always align smoothly with the rapidly evolving landscape of AI technologies. Additionally, the efficacy of AI relies on substantial data volumes, crucial for system training, but acquiring diverse data from various providers can entail additional expenses for companies.