AI’s role in medical breakthroughs: How GPUs are transforming genomics and drug discovery

In the rapidly-evolving field of medical research, artificial intelligence (AI) is proving to be a game-changer, particularly in genomics and drug discovery. Leveraging the immense computational power of GPUs (Graphics Processing Units), researchers are accelerating the pace of medical breakthroughs, reducing the time and cost required to analyze complex biological data.

Speaking at DevSparks 2025, Gaurav Tripathi, Co-founder and Chief Technology Officer of InnoPlexus, emphasised how AI-driven advancements in computational biology are enabling scientists to make discoveries at an unprecedented scale. “Solving real-world problems with AI, especially in life sciences and healthcare, has been our focus. The exponential acceleration in data generation is where we see the biggest challenge and opportunity,” he said.

Impact of AI on genomics

Genomics is one of the most significant areas where AI and GPUs are making a difference. The human genome consists of approximately three billion base pairs, and sequencing this data traditionally took weeks or even months. However, with the use of GPUs, the process has been drastically accelerated.

“Just for one whole genome sequence, we are looking at 180 gigabytes of data,” Tripathi noted.

“Using traditional approaches, generating variants from this data could take weeks or months. With GPUs, we’ve managed to generate more variants at 25 times the speed," he said.

Beyond speed, AI has also helped in cost reduction. Conventional methods required nearly $35,000 per genome analysis, but AI-driven models running on GPUs have lowered the cost to under $2,000, making high-quality genomic research more accessible.

Drug discovery and AI

AI’s impact extends beyond genomics into drug discovery, where it is streamlining the identification of potential new treatments. Tripathi shared how AI-assisted research has been instrumental in analysing the effectiveness of Alzheimer’s drugs.

“We analysed data from 1,500 blood samples collected from 500 patients at different time points. The AI-driven insights helped validate the efficacy of the drug and demonstrated that patients with certain genomic profiles responded better to the treatment,” he explained.

Another breakthrough has been in the study of protein-protein interactions, an essential aspect of understanding disease mechanisms. “Using AlphaFold3 and GPUs, we predicted 126 million protein-protein docking interactions within just four days. Traditional methods would have taken nearly ten months,” Tripathi stated.

These advancements have significant implications for personalised medicine. By understanding how specific genetic profiles interact with treatments, doctors can tailor therapies to individual patients, improving outcomes and reducing trial-and-error prescriptions.

One of the most promising AI-driven studies by InnoPlexus involved pancreatic cancer, a disease notorious for its rapid progression and high mortality rate. Tripathi cited the case of a preclinical drug, ETC-569, being developed in collaboration with Singapore’s drug discovery and development center EDDC. “Pancreatic cancer is often a death sentence, with patients surviving only a few weeks post-diagnosis. Using AI, we identified potential applications of this drug in treating pancreatic and colorectal cancers,” he said.

The future of AI in healthcare

As AI continues to impact healthcare, the focus is shifting from merely using AI to actively building AI solutions. Communities like the GPU Developer Community, initiated by InnoPlexus, are fostering innovation by enabling experts to collaborate on developing next-generation AI models for healthcare.

“AI is not just about using ChatGPT. It’s about solving real-world problems that impact lives,” Tripathi concluded.

With AI-driven breakthroughs in genomics, drug discovery, and precision medicine, the healthcare industry stands at the brink of a new era—one where disease treatment is faster, more accurate, and accessible to all.