Author

Hammed Khakzad

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Open access Oct 2026

SIMORGH: Ensemble-Aware Geometric Deep Learning for Apo-State and Cryptic Ligand Binding Site Prediction

SIMORGH consistently outperforms existing static and dynamics-aware methods under the challenging ligand-free setting, while identifying cryptic binding pockets, maintaining strong prediction consistency between paired apo and holo structures, and scaling efficiently to large multi-chain complexes.

Omid Mokhtari, Amirhosein Naghsh-Nilchi, B. Grüning et al. · bioRxiv · 0 citations

Open access Aug 2026

BindCORE: Biophysical Ensemble Learning for Predicting Interaction Sites in Intrinsically Disordered Regions

BindCORE is introduced, an ensemble-aware deep learning framework that integrates global, local, and pairwise biophysical descriptors to predict interaction sites within IDRs and shows that conformational ensembles contain interpretable determinants of LIP and MoRF binding residues.

Nicolas Buton, Luiz Felipe Piochi, Hammed Khakzad · bioRxiv · 0 citations