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The authors introduce DD-03B, a large-scale database of all-atom dissociation trajectories for 19,037 ligand-protein complexes derived from molecular dynamics simulations. By computing dissociation rate constants through trajectory reweighting, they categorize complexes into three mechanistic types, offering insights into kinetic characterization. This resource addresses the lack of dynamic data in drug design and provides a foundation for training AI models to predict drug-protein dissociation kinetics.
Forget static structures: this massive database of 40TB of dynamic simulations reveals three distinct mechanistic types of ligand-protein dissociation, paving the way for AI-driven drug design.
Understanding the kinetics of drug-protein interactions is paramount for drug design, yet the field lacks large-scale, dynamic data to move beyond static structural analysis. Here, we present DD-03B, a massively scalable database providing dynamic, all-atom dissociation trajectories for a broad set of ligand-protein complexes. Utilising and extending a validated computational pipeline, we generated dissociation trajectories for 19,037 ligand-protein complexes sourced from PDBbind+v2020R1, resulting in a repository of approximately 0.3 billion simulation frames totalling 40 TB in size. For these systems-which possess experimental binding affinities (kd) but typically lack measured koff rates-we computed and assigned dissociation rate constants through trajectory reweighting. Our analysis reveals that protein-ligand complexes can be categorised into three mechanistic types (pathway-dominant, open-pocket, and entropy-pocket systems), each requiring distinct strategies for accurate kinetic characterisation. Together with our previously released DD-13M, DD-03B forms the core of the expandable Dissociation Dynamic Database (DDD) project, which will be continuously augmented with new trajectories. This large-scale, publicly available resource establishes a critical foundation for training and benchmarking next-generation generative AI models to predict and optimise drug-protein dissociation kinetics.