The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.

Protein neddylation is catalyzed by a three-enzyme cascade, namely an E1 NEDD8-activating enzyme (NAE), one of two E2 NEDD8 conjugation enzymes and one of several E3 NEDD8 ligases. The physiological substrates of neddylation are the family members of cullin, the scaffold component of cullin RING ligases (CRLs). Currently, a potent E1 inhibitor, MLN4924, also known as pevonedistat, is in several clinical trials for anti-cancer therapy. Here we report the discovery, through virtual screening and structural modifications, of a small molecule compound HA-1141 that directly binds to NAE in both