Insights

The life sciences sector is uniquely vulnerable to the inherent risks of AI-generated evidence in drug development and discovery. This client alert focuses on five core challenges: hallucinations, skewed predictions from unrepresentative training data, the opacity of AI reasoning, input sensitivity that undermines reproducibility, and adversarial manipulation of training data. To address these risks, companies should use experimental validation and human oversight as the common legal and regulatory safety net. This entails among other measures auditing AI outputs, characterizing training data, and structural oversight. Companies also should prepare for evolving frameworks and guidance from the U.S. Food and Drug Administration (FDA) and the European Union (EU) that address use of AI in drug discovery and development .  

On August 31, 2026, the U.S. Securities and Exchange Commission (SEC) and the U.S. Food and Drug Administration (FDA) signed a Memorandum of Understanding (MOU) establishing a formal framework for the two agencies to share non-public information concerning FDA-regulated products and the companies and individuals engaged in their manufacture, distribution, and sale. The MOU was signed by SEC Chairman Paul S. Atkins and Acting FDA Commissioner Kyle Diamantas. It takes immediate effect and runs for three years, subject to renewal, modification, or termination. In a press release, Chairman Atkins described FDA-related disclosures by public companies as having “a significant impact on our markets” and identified the FDA as “a valuable partner in our efforts to administer and enforce applicable disclosure requirements under the federal securities laws.” Acting Commissioner Diamantas framed the MOU as protecting “both the patients who rely on FDA-regulated products, and the public trust that drives health care innovation.”

The rapid emergence of AI/ML tools in drug discovery, from target identification to generative molecular design, lead optimization, and repurposing, has transformed how life sciences companies build some of their most valuable assets. As AI/ML tools take on an increasingly significant role in the drug discovery process, companies face a set of largely unresolved IP challenges that require careful planning and proactive governance.