Active Machine Learning for Drug Discovery & Nanomedicine with Dr. Daniel Reker

Can artificial intelligence help make cancer therapies safer, more targeted, and more effective?

In this episode of BioTalk Unzipped, Gregory Austin sits down with Dr. Daniel Reker, Assistant Professor at Duke University, for a wide-ranging conversation on active machine learning, nanomedicine, drug delivery, and the future of AI in biomedical research.

This episode is brought to you by Leucentra.

Inspired by Science Empowered by IT

https://leucentra.com/

Dr. Reker works at the intersection of AI, chemistry, biomedical engineering, pharmacology, and molecular medicine. His lab develops computational and experimental approaches to better understand small molecules, nanoformulations, and drug delivery systems.

The conversation explores how machine learning can support drug discovery and development, especially in areas where datasets are small and the biology is complex. Dr. Reker explains why nanoformulations may be able to improve targeted drug delivery, reduce toxicity, and potentially revive therapeutic agents that previously failed because of safety or tolerability issues.

Gregory and Dr. Reker also discuss explainable AI, the risks of black box thinking, AI bias, predictive modeling, FDA considerations, non-animal models, and the responsible use of AI in education and science.

Topics include:

• Active machine learning in drug discovery

• AI and nanomedicine

• Cancer therapy and targeted drug delivery