AI In Medicine: Can India Lead The Next Pharma Revolution?

The integration of artificial intelligence (AI) into biotechnology and molecular biology is revolutionising drug discovery, personalised medicine, and therapeutic innovation. AI and advanced analytics are helping researchers develop new treatments like antibody-drug conjugates (ADCs), gene-editing tools, and cell-based therapies faster. 

For India, this is a big opportunity to move beyond being the "pharmacy of the world" for generic drugs and become a leader in biopharmaceutical innovation. 

With strong government support in AI infrastructure, updated regulations, and industry partnerships, India can strengthen its position in the pharmaceutical sector. 

However, challenges like skill shortages, ethical concerns, and fair access to these advanced treatments must also be addressed.

The Transformative Impact of AI on Biotechnology and Molecular Biology

AI is making drug discovery faster and cheaper by improving research methods. Scientists use AI to refine treatments for diseases like diabetes and muscular dystrophy, continuously improving results through data analysis. 

AI can also predict protein structures, helping researchers find new drug targets without years of trial and error. This is especially useful for complex drugs like ADCs, where AI helps determine the best antibody-toxin combinations and dosages. 

AI also enhances gene-editing techniques like CRISPR-Cas9 and CAR-T cell therapy by predicting unwanted side effects, making treatments for diseases like sickle cell anemia safer. In India, companies like Biocon and Sun Pharma are using AI to speed up drug screening and find new uses for existing medicines.

The fusion of AI with molecular biology is creating personalized treatments based on a person’s unique genetic makeup. Advanced AI models can analyze large biological data sets to find disease markers, helping doctors choose the right treatments for conditions like cancer and rare genetic disorders. For example, AI-powered tools can combine genetic data with medical records to quickly diagnose diseases, reducing delays in treatment. India is also making progress in genomics with projects like the Genome India Project. 

However, to fully benefit from these advances, India needs better AI systems that can handle large-scale genetic data while keeping patient information secure.

India’s Pharmaceutical Sector: Current Strengths and Strategic Vulnerabilities

India supplies 60 per cent of the world’s generic drugs, supported by robust API production capabilities and cost-efficient manufacturing. This strength, however, is increasingly threatened by pricing pressures and regulatory scrutiny in key markets like the U.S. and EU. 

The sector’s heavy reliance on generics (75 per cent of revenue) leaves it exposed to competition from countries like China and South Korea, which are investing aggressively in AI-driven drug discovery.

Indian firms are also making strides in biosimilars, with over 100 products in development targeting oncology and autoimmune diseases. Biocon’s trastuzumab biosimilar and Dr. Reddy’s rituximab biosimilar exemplify this progress. However, transitioning to novel biologics requires AI tools for target validation and pharmacokinetic modeling—areas where India lags behind global peers.

While the IndiaAI Mission’s Rs 10,300 crore allocation for GPU infrastructure and datasets is a positive step, disparities persist. Most pharmaceutical SMEs lack access to high-performance computing (HPC) clusters needed for AI/ML workloads. 

Regulatory frameworks also lag; for example, India's regulatory framework for AI-based clinical trials is still evolving and lacks the clarity and structure found in the U.S. Food and Drug Administration (FDA) guidelines. The FDA has established a well-defined framework for AI and Machine Learning (ML) Software as a Medical Device (SaMD), which provides specific rules on how AI-driven medical technologies should be developed, tested, approved, and monitored. This ensures that AI-based medical devices maintain safety, transparency, and effectiveness while being continuously improved. 

In contrast, India has introduced ethical guidelines for AI in healthcare through the Indian Council of Medical Research (ICMR), focusing on principles like informed consent, data privacy, and bias reduction.

However, these guidelines do not provide specific instructions on how AI-powered medical devices should be designed, tested, or approved. The lack of a structured regulatory framework creates uncertainty for researchers and companies, slowing down innovation and making it harder for India to compete in global AI-driven healthcare advancements.

Without clear regulations, AI-based clinical trials in India face challenges in approval processes, compliance, and international collaboration. To fully harness AI’s potential in healthcare, India needs a more structured regulatory system that balances innovation with patient safety and ethical AI usage.

Strategic Integration of AI: Pathways for India

The IndiaAI Datasets Platform must prioritize curating anonymized genomic, proteomic, and clinical trial data from diverse Indian populations. Collaborations with hospitals and research institutes could emulate the UK Biobank, creating a resource for training diagnostic AI models tailored to India’s disease burden (e.g., tuberculosis, diabetes).

The IndiaAI Innovation Centre (IAIC) should partner with pharma giants and startups to co-develop foundational models for drug discovery. A focus on edge computing can democratize access to AI tools in rural areas, enabling decentralized clinical trials for neglected tropical diseases.

Expanding AI literacy through programs is critical. Integrating AI modules into pharmacy and biotechnology curricula, establishing certification programs for AI-augmented drug manufacturing and creating ethical review boards to oversee AI applications in sensitive areas like germline editing are all need of the hour.

What should India do?

First, the government should triple R&D spending, increasing allocations from 0.7 per cent to 2 per cent of GDP, specifically targeting projects that converge AI and biotechnology. 

Second, to attract multinational investment, India must modernize its intellectual property (IP) frameworks by implementing data exclusivity provisions and fast-tracking patents for drug candidates developed using AI. 

Third, domestic innovation should be actively incentivized by providing tax rebates for the adoption of AI toolkits and establishing a Rs 5,000 crore venture fund dedicated to AI-driven biotech startups. 

India stands at a crossroads: leverage AI to ascend the biopharmaceutical value chain or risk ceding ground in the innovation economy. By aligning India's mission to improve the compute infrastructure with pharmaceutical priorities, and by fostering academia-industry consortia, and enacting forward-looking policies, India can emerge as the world’s first AI-powered biotech superpower.

Ravishankar is a microbiologist, political analyst and commentator. He tweets at oru_pavam_nair.