E234: The contrarian case for physics over data: can deterministic, training-free models beat ML in lead optimization?

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 that help determine biological function. The key message is that AI and physics should not be seen as enemies. In data-sparse areas such as rare diseases, novel targets and first-in-class chemistry, physics-led methods may offer a complementary route to insight, especially where machine learning has little or no training data to rely on. Topics Covered Why pharma’s AI gold rush may miss key biology The principle of incompatibility Physics-first drug discovery Quantitative complexity theory Why explainability matters Molecular dynamics and information flow Atomic and amino acid participation factors Complexity hotspots in molecules Static structures versus molecular motion Rare disease and data-sparse discovery About Eularis Eularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies. Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work. The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges. AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny. AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint. Start with the Institute → https://eularis.com/institute/ Everything else → https://eularis.com Dr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X