Hello, we’re the HITS HyperLab team. These days, global leaders are focusing on countless technologies and industries, but one area stealing the spotlight is undoubtedly AI-driven drug discovery. The 2024 Nobel Prize in Chemistry awarded to Demis Hassabis, CEO of Google DeepMind, is a testament to this. His creation, AlphaFold, has brought groundbreaking changes to the life sciences. So, when did AI drug discovery begin? Where does it stand today, and what might its future look like? In this post, we’ll take a journey through its past and present while envisioning what lies ahead.
In 1928, a scientist stumbled upon a mold that led to the creation of penicillin, the world’s first antibiotic. That discovery was a stroke of luck, but humanity no longer relies on chance to combat diseases—we’ve forged a powerful tool called drug discovery. Today, artificial intelligence (AI) is revolutionizing this tool, earning global attention. AI is accelerating the drug development process, cutting costs, and unlocking the potential for personalized treatments. NVIDIA’s CEO, Jensen Huang, captured this shift perfectly when he said, “If I were a student again, I’d study human biology,” highlighting how the fusion of AI and biology could reshape the future.
The history of AI-driven drug discovery has unfolded alongside the evolution of computer technology. To be precise, early computer-based drug development differs from today’s AI, but it can be viewed as the foundational technology that paved the way for modern AI drug discovery.
Looking back, the history of applying modern AI techniques to drug discovery is relatively short. However, the rapid pace of progress in that brief time is remarkable, driving transformative innovation in drug development.
In 2025, AI-driven drug discovery has firmly established itself as a cornerstone of the biotech industry. Large-scale projects are emerging rapidly across the globe.
Today, AI drug discovery stands at a pivotal moment of global competition and technological innovation.
So, how will AI-driven drug discovery evolve moving forward? Many experts predict that AI will dramatically enhance both the speed and precision of drug development. Beyond that, by integrating with robotic technology, it could automate the entire process from design to production and even enable the creation of personalized drugs tailored to individual patients’ health conditions.
Moreover, OpenAI CEO Sam Altman has expressed strong optimism, stating, “AI will be a tremendous boon to human health.” As such, AI is poised to lead every stage of drug discovery in the future, establishing itself as a vital national industry. This will inevitably intensify global competition. However, despite recognizing its importance, individual companies face significant challenges in spearheading AI drug discovery on their own.
The process demands enormous capital, including costs for GPU servers, skilled personnel, and program maintenance. For instance, the United States is planning a $500 billion ‘Stargate’ project, while Japan intends to invest roughly $150 billion in AI and related industries. Competing with such massive investments can be daunting for individual companies. As a result, rather than building an in-house AI drug discovery system from scratch, leveraging an existing, well-developed AI drug discovery SaaS platform could offer a practical and efficient alternative.
One solution worth noting at this juncture is HyperLab, a domestically developed platform from Korea. Built on HITS’s proprietary technology, PIGNet, HyperLab enables simpler and more accurate predictions of binding affinity between target proteins and molecules. PIGNet stands as the world’s first docking technology to fuse AI with physics, blending data-driven approaches with physics-based methodologies to maximize their respective strengths. (For more details, check out this blog post.)
HyperLab doesn’t stop at existing technologies; it continuously develops new tools to aid drug discovery. This April, it will unveil ‘Hyper Binding Co-folding,’ an innovative technology that accurately predicts ligand-protein binding structures and energies using only protein sequences and molecular structure data.
Additionally, HyperLab plans to introduce a proprietary LLM (Large Language Model) system called ‘AI Assistant’ for drug discovery researchers. While numerous LLM programs already exist, HITS’s AI Assistant is anticipated to provide practical support by incorporating the latest research papers in the field.
Lastly, we present ‘Screening X,’ a tool designed to identify active compounds from a massive library of 7 trillion molecules. Traditional methods made screening such a vast number practically impossible—even a supercomputer would take over 100 years. HITS, however, has turned this into reality by maximizing speed with a groundbreaking search algorithm.(For more details, check out this blog post.)
Drug discovery began with chance discoveries in the past, evolved into today’s innovations, and is set to become a powerful tool for safeguarding human health in the future. Platforms like HyperLab could be the perfect starting point for that journey.
AI-Powered Drug Discovery Platform: HyperLab
