About this episode

Dr. Jacek Marczyk discusses the potential of physics-led methods in drug discovery over traditional data-driven approaches.

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dr. Jacek Marczyk, co-founder and CEO of BioDynLab, about a contrarian view of computational drug discovery: that the next leap may come not from more data and bigger models, but from physics. Dr. Marczyk brings a background in aerospace engineering, automotive, Silicon Graphics and complexity science. His work led to quantitative complexity theory, which he now applies to molecules through BioDynLab’s deterministic, training-free approach. The conversation explores why high precision and high complexity cannot coexist, and why throwing more compute at biological problems does not automatically produce useful knowledge. Dr. Marczyk argues that machine learning can produce impressive outputs, but without explainability, teams may get a result without understanding the physics behind it. He explains how BioDynLab uses molecular dynamics and complexity theory to study how atoms and amino acids move, how information flows through molecules, and which residues act as key “hotspots” in that dynamic system. Instead of treating molecules as static structures, this approach looks at the motion and information patterns…

People in this episode

Host: Dr Andree Bates

Guest: Dr. Jacek Marczyk

Topics covered

• computational drug discovery
• deterministic models
• machine learning
• molecular dynamics
• complexity theory
• biological function

Keywords

• drug discovery
• deterministic models
• machine learning
• molecular dynamics
• complexity theory
• biological function
• AI
• rare diseases

Mentioned in this episode

Organizations: BioDynLab

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