AI and Ramanomics Could Transform Cancer Drug Discovery

A New Window into Living Cells

For decades, studying cancer cells has largely meant looking at snapshots — fixed, stained, and static. But a new approach, blending artificial intelligence with a technique called Raman spectroscopy, promises to change that. Experts say watching cancer cells live could transform how drugs are discovered and tested.

At the centre of this shift is Parmita Mishra, whose venture Precigenetics is pairing AI with chip technology to understand living cells in real time. The method, dubbed 'Ramanomics,' uses light scattering to capture the chemical fingerprint of a cell without destroying it. This is a significant departure from traditional methods that often kill or alter the cells being observed.

What Is Ramanomics?

Raman spectroscopy itself is not new. It has been used in labs for years to identify molecules by how they scatter laser light. But it produces enormous amounts of data, and interpreting that data has been a bottleneck. That is where AI comes in.

Mishra's team has built a platform that uses machine learning to make sense of the complex spectral information. The result is a way to monitor living cells continuously — watching how they respond to drugs, mutate, or interact with their environment. It is a technique that could eliminate a major obstacle to studying live cells: the trade-off between detail and viability.

Why Watching Cells Matters

Static imaging can show what a cell looks like at a given moment, but it cannot capture the dynamics of disease. Cancer cells are constantly changing, adapting, and resisting treatment. Being able to observe these processes in real time could give researchers a far more accurate picture of how a drug works — or fails.

This matters for drug discovery, where the failure rate is notoriously high. Many compounds that look promising in petri dishes fail in human trials because they behave differently in live systems. Watching cells live could help researchers catch problems earlier, saving time and money.

From Lab to Industry

Mishra's work has been highlighted in multiple outlets, including The Times of India and Buffalo News, as part of a broader push toward AI-driven biology. Precigenetics is positioning itself at the intersection of microfluidics, optics, and machine learning — a space that is attracting growing interest from both academic labs and pharmaceutical companies.

The potential applications go beyond cancer. Ramanomics could be used to study bacterial infections, neurodegenerative diseases, or even to screen for drug toxicity in organ-on-a-chip models. But cancer remains the immediate focus, given the urgency and scale of the problem.

Still, experts caution that the field is young. The technology must prove itself in rigorous, large-scale studies before it becomes a standard tool. Regulatory hurdles and the cost of adoption are also factors that could slow its path to widespread use.

What to Watch Next

As Precigenetics and others refine their platforms, the key will be validation — showing that insights from live-cell imaging translate into better drugs in the clinic. Keep an eye on partnerships with major pharma companies and peer-reviewed studies in the coming months. If the results hold up, watching cancer cells live could move from a niche technique to a cornerstone of drug discovery.