'AI-led R&D reshaping speed & decision-making in India's Bio-Pharma sector': Report

AI-led R&D is reshaping speed and decision-making in India's bio-pharma sector, according to an EY-Parthenon India report released at the two day BioAsia conference, which kicked off in Telangana on Tuesday.

The report highlighted the rise of the AI-native R&D stack, a layered architecture spanning scientific intelligence, generative design, digital twins, automated experimentation, real-world evidence integration, and regulatory traceability.

India's pharma ecosystem runs deep, with over 900,000 professionals across research, engineering, and manufacturing. Its CDMO market, valued at $7.9 billion in 2024, is projected to nearly double by 2033. With 23 of the top 50 global life sciences firms operating GCCs here, India is embedding AI, clinical analytics, and digital innovation into global pipelines.

Modern development models now run discovery, CMC, clinical, and regulatory workstreams in parallel, replacing linear handoffs. Scientific copilots synthesize literature and multi-omics data in minutes, while generative models design molecules before synthesis. Digital twins simulate biological and manufacturing scenarios before capital commitments. R&D speed is no longer just about quicker tests—it's about smarter, faster decisions powered by linked data and safe AI flows.

Instead of assessing one compound at a time, teams now evaluate multiple options simultaneously using shared, dynamic datasets. Large language models (LLMs) and agents provide deeper insights into viable targets and protein behavior under varying conditions. Modern tools, leveraging GenAI, graph-based approaches, NLP, and physics-driven simulations remove traditional bottlenecks, helping researchers assess targets, design stronger molecules, and lower preclinical risks.

"Across the value chain, drug research is changing as digital tools reshape how work is planned and executed," the report said. "Two broad categories of LLMs are evolving one Scientific Copilots and Operational Copilots. New AI platforms with embedded copilots enable precise engineering of therapeutic molecules that selectively target tumors while minimizing off-target effects—addressing a key challenge in cancer treatment. Beyond discovery, these copilots guide decisions across research, clinical development, and regulatory preparation, fostering connected, data-driven coordination throughout the R&D lifecycle," said the report titled "Pharma’s New Architecture: Where Novel Science Meets AI and Manufacturing Power."

The analysis also spotlighted a structural shift in pharmaceuticals, as companies move from incremental, product-by-product development to integrated, reusable platforms blending discovery science, data intelligence, and scalable manufacturing. This redefines speed, predictability, and competitiveness for next-generation therapies.

"Indian biopharma is undergoing a structural reset," said EY-Parthenon India's National Lifesciences Leader, Suresh Subramanian. "Scientific breakthroughs alone aren't enough. Winners will integrate discovery, AI-native intelligence, and manufacturing into disciplined, repeatable platforms. The shift from one-off products to reusable engines—from mRNA and CRISPR to AI-driven design stacks—is redefining speed, reliability, and scale. It's about building systems that compound learning and deliver therapies consistently. This positions India for large molecules and new modalities where bigger opportunities lie ahead," he added.

The report added that as biologics and advanced therapies grow, manufacturability is no longer downstream. CMC decisions now shape discovery from the start. Early integration of science, digital modeling, and production planning boosts reliability, quality, and cost control. Supply continuity has become a core design requirement, not a fix—especially for advanced modalities needing specialized inputs like vectors, enzymes, and high-potency payloads.