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AI is transforming global drug discovery. As China races ahead and the US recalibrates, India must harness its talent and data to drive pharma innovation.
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The global pharmaceutical landscape is at a critical juncture. Traditional approaches to drug discovery face constraints due to lengthy timelines, high costs, and uncertain outcomes. Estimates indicate that drug development takes 10-15 years, with research and development (R&D) for a new product requiring at least US$2.5 billion. The use of Artificial Intelligence (AI) in drug development has been identified as a way to accelerate the process—predicting drug properties, optimising clinical trials, and formulating personalised medicines. As China makes rapid advances in this sector and the United States (US) undergoes structural transitions, the landscape presents both a challenge and an opportunity for India to leverage its talent pool, patient datasets, and biotechnology policies to drive AI-assisted drug discovery and transition from the ‘pharmacy of the world’ to an innovation-based pharmaceutical system.
Drug discovery is essential as emerging diseases and drug resistance challenge existing treatments, understanding of disease mechanisms and healthcare needs deepens, and the demand for affordable therapies increases. For any research organisation or company, advancing a drug candidate to Phase I clinical trials is a tremendous feat; however, 90 percent of drug candidates fail during Phase I, II, or III of the clinical trials. Most of these failures occur during Phase I trials owing to issues in toxicity and efficacy, indicating that rigorous optimisation is needed to identify ideal drug candidates.
AI-based techniques hold considerable potential to transform this landscape by enabling accurate and efficient analysis of large datasets, predicting the properties and functions of novel drug candidates, and enhancing the efficacy of clinical trials through improved trial design.
With global spending on medicines likely to exceed US$1.9 trillion by 2027 due to ageing demographics and an increased burden of diseases, the need for innovation in drug discovery has never been greater. The process of developing new medicines is complex and labour-intensive, often taking decades and relying heavily on trial-and-error experimentation. AI-based techniques hold considerable potential to transform this landscape by enabling accurate and efficient analysis of large datasets, predicting the properties and functions of novel drug candidates, and enhancing the efficacy of clinical trials through improved trial design.
The breakthrough in AI-life sciences occurred when DeepMind’s AlphaFold predicted protein structures from amino acid sequences in 2018. This was followed by the use of AI in mRNA vaccine research during the COVID-19 pandemic, and the development of personalised medicine by integrating AI with multi-omics data in 2023. This catalysed a new phase in life science innovation with drug companies partnering with tech firms to leverage AI healthcare solutions. For instance, Eli Lilly and Novartis have partnered with Isomorphic Labs to advance Google Deep Mind’s Alphafold technology; EvolutionaryScale – an AI startup that focuses on life biology – raised US$142 million in funding in 2024 and is partnering with Amazon Web Services and NVIDIA for drug discovery research; and Sanofi will implement BenchSci’s generative AI platform – Ascend – that is specialised for disease biology across Sanofi’s global sites.
In 2024, India’s Aurigene introduced an AI/Machine Learning (ML)-enabled drug discovery platform to reduce the drug development timeline by 35 percent, marking a significant advancement.
The world’s first completely generative AI-discovered drug, rentosertib, was developed by Insilico Medicine. The company applies a completely AI-driven drug discovery platform for screening drug candidates. AstraZeneca was able to reduce its drug discovery timeline using BenevolentAI. Furthermore, Roche has leveraged the Food and Drug Administration (FDA) Modernisation Act 2.0 for non-animal model-based testing by applying organoid-on-chip and AI for understanding drug toxicity. In 2024, India’s Aurigene introduced an AI/Machine Learning (ML)-enabled drug discovery platform to reduce the drug development timeline by 35 percent, marking a significant advancement.
Most large language models (LLMs), such as ChatGPT, have been aiding researchers with literature surveys, bioinformatics, statistics, and, oftentimes, as a lab assistant, while Chan Zuckerberg’s rBio is an LLM that enables users to pose complex challenges. This AI-based cellular model provides digital representations of cell behaviour, potentially offering insights into how drugs affect cellular activities. Most recently, Google’s C2S-Scale 27B foundation model – based on Gemma and developed with Yale University- offers critical information on cancer cell behaviour, potentially enabling the development of new pathways for cancer drug therapies.
China increasingly exemplifies a ‘DeepSeek moment’ in life sciences as it emerges as a global competitor. Presently, China is the frontrunner in AI-driven drug discovery patents. Multibillion-dollar deals between global pharmaceutical giants—Sanofi, Pfizer, AstraZeneca, and Eli Lilly—and Chinese AI biotech firms have boosted China’s drug discovery capabilities, signalling a shift from generic manufacturing to innovative drug development. China’s ascent is fueled by considerable state and private investments in life science R&D, dedicated policies to expand the country’s manufacturing capacity, prioritisation of AI in the 2025 Five-Year Plan, access to vast patient datasets for AI training, and a multidisciplinary talent pool reinforced by brain gain policies such as the ‘K visa.’ A vibrant start-up ecosystem coupled with a dedicated national venture capital guidance fund for disruptive technologies has positioned China as a formidable player in AI-assisted drug discovery.
China is the frontrunner in AI-driven drug discovery patents. Multibillion-dollar deals between global pharmaceutical giants—Sanofi, Pfizer, AstraZeneca, and Eli Lilly—and Chinese AI biotech firms have boosted China’s drug discovery capabilities, signalling a shift from generic manufacturing to innovative drug development.
On the other hand, the US—the global leader in life sciences—is undergoing structural changes that threaten to hamper the AI-drug innovation nexus. Funding cuts to the National Institutes of Health and National Science Foundation, stricter visa regulations, and looming revenue losses from expiring drug patents for pharmaceutical companies are only a few examples of innovation slowing down.
For India, these developments highlight both a challenge and an opportunity: as China strengthens its AI-driven drug discovery capabilities, and global tech firms and pharma companies seek investment opportunities, India must strategically leverage its own talent pool, patient datasets, and life science policies – such as BioE3 – to remain competitive in the AI-assisted drug discovery landscape.
India recognised the potential of integrating AI with life sciences with the launch of the first AI-biology symposium during the Genomics India Conference in August 2025 at the Indian Institute of Science (IISc), while AI featured prominently at the world’s leading life sciences event – Bio Asia 2025 – in Telangana. The Department of Biotechnology and BIRAC led a workshop on Bio-AI to further India’s BioE3 policy. Global Capability Centres (GCCs) in India will adopt AI for drug development, while Special Economic Zones (SEZs) and biotechnology parks will provide infrastructural support. Furthermore, Karnataka and Telangana are poised to serve as hubs for life science GCCs, with state governments supporting the set-up of Centres of Excellence for R&D. Notably, Hyderabad’s Novartis Biome has focused on integrating AI into pharma R&D. In addition, Bristol Myers Squibb has invested US$100 million to launch a GCC for AI in drug discovery. Most recently, Google’s parent company, Alphabet, announced a US$15 billion investment in an AI hub in Visakhapatnam to drive AI innovation across India. Furthermore, with India possessing the second-largest GenAI startup hub, the application of AI in life sciences will likely expand. As India prepares to host the AI Impact Summit in February 2026, these steps indicate that India is well-positioned to enhance AI applications, including drug discovery, strengthening its global standing.
As China strengthens its AI-driven drug discovery capabilities, and global tech firms and pharma companies seek investment opportunities, India must strategically leverage its own talent pool, patient datasets, and life science policies – such as BioE3 – to remain competitive in the AI-assisted drug discovery landscape.
Several challenges and limitations to AI in drug discovery need consideration. Large volumes of high-quality and consistent data are necessary for training purposes and to ensure the accuracy and reliability of experimental outcomes. Biases in datasets can exacerbate healthcare disparities if certain demographic groups are underrepresented, potentially leading to poor drug efficacy or safety issues in these groups. Siloed data can hinder sharing and collaboration, leading to ‘hallucinations’ or misleading outcomes from inconsistent data. Machine learning’s ‘blackbox problem’ – the inability to understand how deep learning carries out its decision-making – raises questions over the fairness and reliability of AI-driven decisions.
Furthermore, the risk of adversarial attacks – where deceptive data is introduced into training sets to manipulate AI models – poses a significant threat to health and safety. Finally, the convergence of AI in healthcare presents unique ethical, legal, and social considerations. Collectively, these challenges demonstrate that AI-driven drug discovery requires regulatory and accountability frameworks to ensure that its application is comprehensive, robust, and achieves the wider goal of improving patient outcomes in a safe, ethical, and efficient manner.
As regulatory frameworks for AI are being developed globally, comprehensive frameworks for the convergence of AI and life sciences remain limited. While the European Union and Japan have legislation governing AI development and deployment, the US FDA has released guidelines on AI-powered medical devices and on leveraging AI for biological products, including drugs.
AI-driven drug discovery requires regulatory and accountability frameworks to ensure that its application is comprehensive, robust, and achieves the wider goal of improving patient outcomes in a safe, ethical, and efficient manner.
India has made progress through the National Strategy for AI for All, the Digital Personal Data Protection Act, 2023, and the Ayushman Bharat Digital Mission. Yet, there is still a need to formulate frameworks for AI-life science convergence. Establishing these frameworks is imperative to align India with growing global benchmarks, ensuring AI-driven drug discovery initiatives remain competitive, innovative, and ethically robust.
The convergence of AI and drug discovery presents India with a unique opportunity to redefine its pharmaceutical sector. While China advances rapidly with state and private-backed investments and focused policies, and as the US navigates structural constraints, India’s strengths lie with its growing talent ecosystem, biotech policies, and international partnerships. India must establish robust regulatory frameworks aligned with growing global standards to ensure transparent, safe, and fair application of AI. By combining innovation with governance, India can accelerate drug discovery, foster equitable healthcare solutions, and secure a competitive position in the global life sciences landscape.
Lakshmy Ramakrishnan is an Associate Fellow with the Centre for New Economic Diplomacy, Observer Research Foundation.
The views expressed above belong to the author(s). ORF research and analyses now available on Telegram! Click here to access our curated content — blogs, longforms and interviews.
Lakshmy is an Associate Fellow with ORF’s Centre for New Economic Diplomacy. Her work focuses on the intersection of biotechnology, health, and international relations, with a ...
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