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

About this episode

This episode features a discussion on the role of active machine learning and nanomedicine in improving cancer therapies 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…

People in this episode

Host: Gregory Austin

Guest: Dr. Daniel Reker

Topics covered

• active machine learning
• drug discovery
• nanomedicine
• cancer therapy
• targeted drug delivery
• AI in biomedical research

Keywords

• artificial intelligence
• drug delivery systems
• nanoformulations
• predictive modeling
• AI bias
• explainable AI
• FDA considerations
• non-animal models

Sponsors

Leucentra

Mentioned in this episode

Organizations: Duke University

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