Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions
Matthew R. Masters; Amr H. Mahmoud; Markus A. Lill · 2025 · Nature Communications
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of proteins interacting with small-molecules, nucleic-acids, and other proteins. Despite these advanced capabilities and broad potential, the current study presents critical findings that question the adherence of these models to fundamental physical principles. Through adversarial examples based on established physical, chemical, and biological principles,
Abstract by Matthew R. Masters; Amr H. Mahmoud; Markus A. Lill, Nature Communications (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41467-025-63947-5