UniPocket: Unified Ligand and Cryptic Pocket Prediction from Protein Language Model Embeddings

Abstract

Identifying druggable pockets in proteins is central to structure-based drug discovery, yet conventional ligand-binding-site prediction and cryptic-pocket prediction are typically treated as separate tasks requiring distinct tools, preprocessing pipelines, and evaluation protocols. This separation is limiting because the two problems share a common biological foundation: co-evolutionary patterns encoded in protein sequences carry implicit signals about both conventional and cryptic binding sites. We present UniPocket, a unified multitask framework for residue-level prediction of both ligand-binding and cryptic-pocket residues within a single shared-backbone architecture. UniPocket uses frozen per-residue ESM-2 embeddings as input to a lightweight residual MLP with two task-specific heads, trained on ligand-contact labels from recently deposited PDB structures and cryptic-pocket annotations from CryptoBench. Training alternates between ligand-labeled and cryptic-labeled mini-batches, while an orthogonality regularizer encourages the two heads to learn complementary rather than redundant signals. UniPocket achieves a macro ROC-AUC of 0.82 for cryptic-pocket prediction and 0.76 for ligand-binding-site prediction — matching or exceeding all dedicated single-task baselines on both tasks simultaneously, without any 3D structural input at inference time.