Drug development has long been a high-stakes, high-cost endeavor. The pharmaceutical industry pours billions into research, only to see more than 90% of compounds fail before reaching the market. Historically, these failures meant shelving promising compounds indefinitely, often due to business or regulatory hurdles rather than a lack of efficacy.
Today, that paradigm is shifting, thanks to artificial intelligence (AI).
On February 18, 2025, Predictive Oncology Inc (NASDAQ: POAI) released results underscoring a fundamental transformation in drug discovery: AI-driven drug repurposing. While AI-based drug development has been a recurring theme for nearly a decade, what sets this moment apart is the combination of hard data, investor optimism, and real-world applications.
While we do not endorse this company, it serves as a compelling example of a significant and transformative trend.
AI is now proving its ability to reassess abandoned compounds, matching them against new targets with unprecedented speed and accuracy. One of the most exciting aspects? Target validation, a traditionally slow and resource-intensive process, is being accelerated by machine learning models that predict efficacy at a fraction of the time and cost of traditional methods.
Pharmaceutical companies spend an average of $2.5 billion per drug approval, yet failure rates remain high. If AI-driven drug repurposing salvages even a fraction of these lost opportunities, the economic and clinical impact could be seismic.
Predictive Oncology’s active learning AI platform recently demonstrated it could identify 10 times the number of viable drug-tumor combinations compared to traditional lab testing. Even more impressively, the platform shaved off an estimated 18 months of wet lab work, accelerating progress in oncology and other critical therapeutic areas.
As AI becomes a central tool in pharmaceutical R&D, regulatory agencies are adapting their frameworks to ensure safety, efficacy, and transparency. In January 2025, The United States Food and Drug Administration (FDA) issued new guidance titled "Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations."
This guidance provides a structured approach for AI-driven drug discovery and medical device approvals, with a focus on:
For pharmaceutical companies, this regulatory clarity reduces uncertainty in AI adoption, making it easier to integrate machine learning models into drug discovery pipelines.
Adding to these industry shifts, Clark Minor, formerly of Palantir Technologies Inc (NASDAQ: PLTR), has recently been appointed Chief Information Officer (CIO) of The United States Department of Health and Human Services (HHS). His expertise in data-driven innovation, AI infrastructure, and life sciences strategy positions him as a key player in shaping the next phase of AI adoption in pharmaceutical research.
Minor’s appointment signals a broader trend of biotech firms hiring AI and machine learning specialists to integrate technology-driven strategies into traditional pharma models. His leadership will likely accelerate efforts in:
In our opinion, his appointment underscores how biotech and AI are converging, setting the stage for a fundamental shift in drug discovery, regulatory strategy, and commercialization.