# A Massively Scalable Ligand-Protein Dissociation Dynamic Database Derived from Atomistic Molecular Modelling

Source: [A Massively Scalable Ligand-Protein Dissociation Dynamic Database Derived from Atomistic Molecular Modelling](<https://www.sota2.com/research/paper/69d732b67d0172140ec76cca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-10T05:00:04.898Z

Authors: Maodong Li, Dechin Chen, Zhijun Pan, Zhe Wang, Yi Isaac Yang

Published: 2026-04-08T07:27:26.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.06761>)

[PDF](<https://arxiv.org/pdf/2604.06761.pdf>)