AI & PHARMA : A NEW ERA IN DRUG DEVELOPMENT
Artificial Intelligence can cut drug discovery time from 5–6 years to just 1–2 years and lower costs by up to 30%
Of all the industries being reshaped by technology, pharma is seeing one of the biggest overhauls, thanks to AI, machine learning, and deep learning. This convergence of computational science and bioscience is revolutionising every facet of the pharmaceutical pipeline, from early-stage drug discovery to clinical trials, manufacturing, and post-market surveillance.
One of the most promising areas of this shift is drug discovery, a process that traditionally spans over a decade and can cost upwards of $2.6 billion, with a high probability of late-stage failures. AI is changing that equation. By mining vast datasets, from genetic sequences and chemical structures to scientific literature and patient records, AI models can now predict the behaviour, efficacy, and safety of drug candidates with remarkable precision.
Startups are at the forefront of this AI-led disruption. Globally, companies like Insilico Medicine, BenevolentAI, Recursion, Atomwise, and Exscientia are setting benchmarks by using AI to fast-track drug development, reduce costs, and boost precision.
India, too, is making significant strides in this domain. Aindra Systems, based in Bengaluru, is harnessing AI for early cancer detection and diagnostics, laying the foundation for data-led drug research in oncology. Qure.ai, although best known for its radiology tools, is increasingly working with healthcare systems to integrate AI in broader clinical workflows, including drug efficacy assessments. Niramai, another Bengaluru-based startup, is using machine learning and thermal imaging to detect breast cancer at early stages, critical data that can feed into oncology drug research pipelines.
According to an IT consultant at Ruby General Hospital, Kolkata, AI is transforming drug discovery and development in pharma by driving efficiency, reducing costs, and compressing timelines. “AI can reduce preclinical timelines by 40–60% and increase early-stage success rates, potentially generating 50+ new therapies worth $50 billion over a decade. AI forecasts drug toxicity, pharmacokinetics, and patient response from historical and ongoing data. It improves trial design, patient recruitment, and success rates as well as lowering adverse outcomes,” he said.
According to Fortune Business Insights, the sector, valued at USD 3 billion in 2022, is projected to grow to USD 7.94 billion by 2030. Other forecasts, such as Roots Analysis, push that figure even higher, predicting the market could hit USD 13.4 billion by 2035—driven by a compound annual growth rate (CAGR) estimated between 12.2% and 16.5% from 2023 to 2035.
At the forefront of this transformation are institutions like Harvard University, where researchers across Harvard Medical School, the Wyss Institute, and the Kempner Institute for the Study of Natural and Artificial Intelligence are developing advanced AI tools to reinvent how drugs are discovered and tested. Their work spans from repurposing existing drugs to designing entirely new molecules using generative models—significantly cutting down on time, cost, and uncertainty in the early stages of drug development.
Closer home, practitioners are already witnessing the ripple effects of this change. “AI-driven medical technologies are evolving into viable solutions for clinical practice and reshaping the dynamics of medical care,” says Dr Siladitya Ray, a well-known psychiatrist and stress management expert based in Kolkata. “Algorithms today can process vast volumes of data from wearables, smartphones, and mobile sensors—changing how medicine integrates into daily life.”
AI is revolutionising clinical trials in the pharmaceutical industry by predicting outcomes more accurately and improving design through the analysis of historic data, precise patient selection, protocol optimisation, real-time monitoring, and advanced post-trial analytics, mentioned the IT expert from Ruby General Hospital. “These functionalities make trials more efficient, cost-effective, and effective, accelerating the availability of new treatments to patients. However, to capitalise on the capability of AI fully, challenges including data quality, regulatory compliance, and ethics have to be surmounted,” he said.
