DFDD: A Cloud-Ready Tool for Distance-Guided Fully Dynamic Docking in Host-Guest Complexation

Fully dynamic sampling of host-guest inclusion remains difficult because conventional docking and conventional molecular dynamics simulations can sample inclusion, but crystal-like binding is typically stochastic and difficult to reproduce. Here, we introduce DFDD (Distance-Guided Fully Dynamic Docking), a cloud-ready implementation of the LB-PaCS-MD framework designed to capture inclusion processes via unbiased molecular dynamics in explicit solvent. DFDD automates system setup, parameter generation, iterative short-cycle MD sampling, and trajectory analysis within a single workflow that runs on Google Colab without any installation. Progress toward complexation is guided only by the host-guest center-of-mass distance, allowing force-free exploration of insertion pathways and enabling the recovery of both stable and transient binding modes. Using β-cyclodextrin as a representative host, DFDD reproduces experimentally observed inclusion geometries within minutes and reveals intermediate states along the insertion route. Optional coupling with pKaNET-Cloud enables pH-aware, stereochemically consistent ligand protonation states prior to simulation, supporting robust host-guest modeling. This Application Note provides a transparent and accessible platform for efficient host-guest complexation studies. The DFDD framework is publicly available at https://github.com/nyelidl/DFDD.