Salhuldin Alqarghuli

This paper benchmarks sequence-based models for predicting protein-ligand binding sites across different ligand availability regimes, showing improved performance with ligand conditioning and chemical language model integration.

This paper introduces a unified model combining a protein language model with an autoregressive transformer to predict protein-ligand binding sites, achieving broad ligand coverage and improved performance for underrepresented ligands.

This paper extends Prot2Token to align protein language models for diverse prediction tasks, improving performance across multiple applications through pre-training and integration with ESM-2.