Can AI predict cancer's next move before drug resistance develops?

Drug resistance remains one of the greatest challenges in oncology drug development. Most targeted therapies are designed to treat today's tumor biology, leaving researchers to react only after resistance mutations emerge. But what if artificial intelligence (AI) could help predict how cancer evolves before treatment begins?

In this episode of Early Signals, recorded live at ASCO 2026, Syneos Wael Harb, MD, Head of R&D and Scientific Strategy in Oncology, speaks with Violet Zahedi, MD, Founder and CEO of Synamics Therapeutics, about how AI, computational biology and protein modeling are being used to anticipate resistance mutations and design more durable cancer therapies.

Drawing on her experience as both a physician and biotechnology founder, Dr. Zahedi explains why oncology has traditionally taken a reactive approach to drug resistance, and how predictive AI platforms could fundamentally change the way targeted therapies are discovered, optimized and commercialized. Rather than redesigning drugs after resistance develops, her team is using machine learning, structural biology and protein modeling to predict cancer's next evolutionary move before it occurs.

The discussion explores the growing role of AI in oncology drug discovery, including its potential to accelerate lead optimization, improve compound prioritization, support biomarker-driven patient stratification and reduce the time and cost associated with developing next-generation cancer therapies. Dr. Zahedi also shares how her team validated its predictive platform using EGFR inhibitors and Aurora A kinase inhibitors, highlighting collaborations with the Mayo Clinic and presenting new findings at ASCO 2026.

Whether you're involved in oncology clinical development, precision medicine, AI-driven drug discovery or biopharmaceutical R&D, this episode explores how predictive resistance intelligence may reshape the future of cancer drug development.

In this episode, you'll learn:

· Why drug resistance remains one of oncology's greatest unmet challenges

· How AI can predict resistance mutations before they emerge

· The role of AlphaFold, computational biology and protein structure prediction in drug discovery

· How predictive resistance modeling may accelerate oncology drug development

· Opportunities to improve compound prioritization and patient stratification

· What AI could mean for the future of precision oncology and targeted therapies

The views expressed in this podcast belong solely to the speakers and do not represent those of their organization.

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