2026-10-10 · Protein–protein binding energy prediction (incl. intrinsically disordered partners) · Journal of Chemical Information and Modeling (ACS)

Interface graph neural network predicts binding energy for folded and disordered partners

graph neural networkbinding affinity predictionintrinsically disordered proteinscomputational screening

FlexENN is a message-passing graph neural network built on a local interface graph with geometry, flexibility descriptors and sequence per residue.
Two models are trained independently: one for folded complexes and one for complexes with a disordered partner, using a newly built IDP dataset.
Benchmarks include tubulin–tubulin interfaces in microtubules and microtubule–MAP complexes.

💡 Points

Affinity predictors trained on folded complexes usually fall apart on disordered partners, so a dedicated IDP set is the real contribution here. For med chem it is protein–protein interface work rather than small-molecule scoring, and I'd test whether it ranks mutations or peptide variants reliably.

FlexENN: A Graph Neural Network for Binding Energy Prediction of Globular and Intrinsically Disordered Protein Complexes ↗