Inside the Work Of Vijay Kumar Naidu Velagala: From Multicellular Models To Antibody Design

Extracting meaningful insights from research is essential for scientific progress, whether it’s for discovering new medical treatments or better understanding complex biological systems.

But as studies become more nuanced and datasets grow larger, research teams are struggling to keep up. Without the right tools to analyze such complex information at scale, critical patterns can go unnoticed, slowing the development of major breakthroughs.

That’s the challenge that data scientist Vijay Kumar Naidu Velagala recognized early in his career. It’s the problem that led him to explore how artificial intelligence, with its ability to uncover hidden trends in large datasets, could become a powerful tool to improve the way researchers work.

Today, he’s leveraging this technology at a leading scientific informatics company, where he helps organizations in biomedicine, drug development, and diagnostics integrate AI into their research workflows. His work allows scientists to make more sense of scattered data, uncover promising treatment options, and bring new therapies and medicines to patients faster.

A Researcher’s Shift Toward AI

While pursuing his master’s in chemical engineering, Vijay began studying the behavior of thin films, which are microscopic layers found in everything from industrial coatings to biological systems. Since these layers are too small to observe directly, he used mathematical models to artificially simulate their behavior, which sparked an interest in how similar tools could help explain the complexity of living systems. During his PhD studies, he used similar techniques to study how cells communicate with one another to form organisms like plants and fungi.

But he soon ran into the same problem that fields like medicine and biochemistry have increasingly faced in recent years: They rely heavily on detailed simulations and complex experimental data, but they lack the capabilities to analyze this rich information at scale. As a result, it’s easy to overlook subtle but critical patterns in complex data, leading to incomplete or misleading conclusions, undermining the reliability of research findings, and slowing progress toward developing new medicines and treatments.

Vijay soon identified machine learning as a particularly promising modality to solve this problem, thanks to its ability to reveal patterns too complex for conventional tools, adapt as new data emerges, and help researchers draw conclusions that are both reliable and replicable in real-world conditions.

Building Scientific AI Tools At Zifo Technologies

Since 2023, Vijay has applied this knowledge as a data scientist at Zifo Technologies, an IT company that helps organizations in research-driven industries like pharmaceuticals, biotech, and agrotech modernize their data platforms and implement artificial intelligence technologies.

Over the years, he’s helped several companies integrate cutting-edge tech into scientific workflows. One system he built helped teams identify promising drug candidates by classifying the properties of chemical compounds early in development. Another project consolidated pharmaceutical research data (like clinical trial results and lab reports) into a single, searchable platform with a standardized format — making it easier to access and analyze.

But one of his most impactful projects was leading the development of an AI platform to support antibody design — a process in drug development that involves creating proteins that can detect and neutralize harmful targets in the body, such as viruses, bacteria, or cancer cells. These antibodies play a vital role in therapeutic treatments, diagnostic tests, and laboratory research.

Traditionally, this process has heavily relied on trial and error, with companies creating antibodies without a full understanding of how they’ll behave under real-world conditions. As a result, studies suggest that as many as 50% may not perform as expected, slowing down the development of critical therapies.

To overhaul this flawed workflow, Vijay worked with a team of data scientists and engineers to develop a platform that could help researchers explore new protein designs faster and more accurately. By fine-tuning AI models with curated datasets of protein sequences — which allows them to understand how an antibody might affect the body — the platform can automatically generate new sequences tailored to specific medical treatments.

This allows researchers to focus on a smaller set of antibody designs with a higher chance of success, rather than testing large libraries of uncertain candidates. “By dramatically reducing experimental turnaround times,” Vijay explains, “this system will actively accelerate antibody research, enabling our partners in the life sciences industry to generate and optimize candidates at a pace previously unattainable — ultimately shortening the timeline from initial discovery to life-saving therapeutic application.”

Putting AI To Work For Real-World Science

Vijay Kumar Naidu Velagala’s work at Zifo Technologies is allowing for more effective use of AI in scientific research. By building systems that can quickly sort through complex data and spot patterns they might have missed, he’s helping research teams deepen their scientific understanding, speed up development timelines, and deliver critical solutions with greater accuracy.

BDG Media newsroom and editorial staff were not involved in the creation of this content.