For decades, pharma has been a race against time. Drug discovery takes years. Clinical trials take years. Regulatory approvals take years. And for every 10,000 molecules explored, perhaps one reaches the market, at an investment of billions of dollars. Meanwhile, patientstoday, living in the Blinkit age, expect instant results, personalised therapies, and better outcomes.

In such a situation, AI actually comes as a boon for the pharma industry. For the Pharma industry, AI is not just another technology. It is changing the rhythm of the industry itself.

Reimagining the Pharma Value Chain

1. Drug Discovery
   Insilico Medicine used AI to identify a new drug for idiopathic pulmonary fibrosis : moving from concept to Phase II trials in less than three years. DeepMind’s AlphaFold solved the 50-year-old protein-folding problem, predicting structures for almost every known protein.
2. Clinical Trials
   AI systems scan millions of health records to find trial candidates in weeks. Adaptive clinical trial designs now adjust themselves in real time, cutting cost and delay.
3. Manufacturing
   Predictive maintenance and computer-vision driven quality control reduces downtime and wastages, creating smarter, self-learning plants.
4. Pharmacovigilance
   AI reviews global adverse-event reports in real time, flagging risks faster than manual teams ever could.
5. Diagnostics & Care
   At Aravind Eye Care, Google’s AI has screened over 6 lakh people for diabetic retinopathy with specialist-level accuracy. Apollo Hospitals and Microsoft’s predictive model now alerts doctors to cardiovascular risk long before symptoms appear.
6. Literature-Based Discovery
   AI platforms like IBM Watson and AstraZeneca’s Literature Graph connect hidden links across millions of studies – turning text into a discovery engine. CSIR’s INDRA system in India is doing this for diabetes and tuberculosis research.
7. Robot Scientists
   Ginkgo Bioworks runs autonomous robotic foundries, where AI decides what to test next and performs thousands of experiments a day with zero fatigue.
8. Patent Filing
   Takeda’s Copilot AI engine is trained on historic filings to pre-populate patent forms, cutting drafting time by 40%.

What are the Challenges

While AI is making rapid progress, there are multiple challenges that need to be addressed so that we can leverage the full power of AI. Some of them are :

• Regulation vs. Innovation: Approval systems aren’t built for self-learning algorithms.
• Data Quality & Bias: Flawed data can amplify inequity.
• Explainability: Doctors can’t rely on black-box models.
• Talent & Culture: Pharma needs scientists who speak code, and coders who respect biology.
• Liability & Ethics: Accountability in AI-informed decisions is still undefined.

The Way Forward

AI will not replace human intelligence; but AI, along with humans, will become a force-multiplier. It is very likely that the next blockbuster drug may not come from a lab, but from a line of code that identified the right molecule faster than any human could. Also, India’s healthcare challenges cannot be solved by money alone. It will need to be solved using AI-driven exponential technologies that compress time and expand access.

Traditionally, Pharma has been science driven. Going ahead, Pharma companies will need to create cross functional teams comprising of scientists who come up a hypothesis, and AI coders who can quickly help test the hypothesis.

Artificial Intelligence is a cosmic gift to mankind – if used wisely, it can make science faster, healthcare fairer, and human life longer.