When it comes to tackling neurodegenerative diseases like Alzheimer's and dangerous viral threats like COVID-19, pharmaceutical companies have no choice but to swing for the fences. But all too often, this means spending billions of dollars researching ineffective products that never hit the market—resources that could be spent pursuing more promising therapeutic remedies.
Until now, the only way to find out what works and doesn't has been through trial and error. But today, emerging tech like artificial intelligence has the power to make the drug development process more efficient than ever before—shortening experimental lifecycles that once took months to mere days.
Such is the work of data scientist Vijay Kumar Naidu Velagala. With a career spanning chemical engineering, data analytics, and bioinformatics, he's now leveraging advanced LLMs and generative protein design models to tangibly shorten development lifecycles and accelerate advanced scientific research.
How AI Brings New Speed to Antibody Discovery
Vijay Kumar Naidu Velagala got his start by studying thin films and the mechanics of multicellular organisms during embryogenesis—the process of embryos folding, dividing, contracting, and stiffening as they grow into larger cell clusters. But as his research grew increasingly complex, Velagala turned to machine learning techniques to make the data more intelligible and uncover insights that would otherwise require thousands of hours of human-powered analysis. Eventually, he shifted his focus from pure scientific research to applying AI in antibody development at Zifo Technologies.
At Zifo, Velagala is developing an AI solution for antibody research that aims to accelerate drug discovery pipelines for scientists at major pharmaceutical companies. In short, he's working to enhance the scientific process of using lab-made proteins to activate the immune system. In this methodology, researchers use injected treatments that bind to specific unwanted cells or viruses—marking them for the human body to target more efficiently with natural immune system responses.
His platform combines different AI frameworks based on protein language models, which are comparable to LLMs that produce content based on predictive analysis. Similar to how LLMs predict text, these models generate novel sequences of amino acids to uncover unique mutations and potential drug candidates that could take weeks to sequence using traditional methods.
"This platform dramatically reduces experimental turnaround times," Velagala explains, "empowering researchers to conduct more experiments, innovate more freely, and ultimately shorten the timeline from initial discovery to life-saving therapeutic application."
Velagala's platform also allows scientists to fine-tune the parameters of different AI models, adding context from previous rounds of internal research (both promising and unsuccessful) and optimizing compute resources to rapidly generate potential antibody sequences and give researchers a head-start on experimentation. The result is a contextual machine learning ecosystem that combines deep biological modeling with production-grade deployment.
Applying AI to Small Molecule Drug Design
Beyond his work with antibody research, Velagala is also designing AI-enhanced research frameworks that address traditional R&D bottlenecks.
In one project, he's using AI to evaluate the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of small molecules for use in clinical treatments. Put simply, small molecule drugs work differently from antibody therapies, and they use specific compounds designed to chemically block or activate biological processes by making changes to DNA within cells themselves. These drugs are typically easy to manufacture and administer, but can also have more common and serious side effects, making ADMET analysis even more important.
Assessing ADMET factors helps scientists prioritize viable drug candidates earlier in the research pipeline, allowing them to choose the most promising experimental drug candidates to reduce failure rates in clinical trials that can derail the approval process.
Velagala also leads a team designing tools to accelerate scientific data retrieval using retrieval-augmented generation (RAG) techniques. By training models on decades of proprietary research, his work can produce verified answers to complex research questions, resulting in up to 30% faster retrieval of relevant data when compared to research assistants manually sorting through archival sources.
By combining accelerated AI-powered drug candidate evaluation with better access to proprietary in-house research data, Velegala's efforts stand to improve how pharmaceutical companies translate scientific insight into life-saving therapies—from discovery to clinical readiness.
Engineering the Future of Drug Discovery
Advanced AI applications are emerging at a critical moment in pharmaceutical development, as threats like antibiotic-resistant pathogens are becoming more common every year, and treatments for neurodegenerative diseases are proving less effective than anticipated.
By leveraging machine learning techniques to accelerate experimentation, Velegala has become a driving force in helping define the future of pharmaceutical research in the AI era.
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