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Updated: Feb 8, 2026

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

Kowit Hengphasatporn1, Lian Duan1, Ryuhei Harada1

  • 1Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan.

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|February 7, 2026
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Summary
This summary is machine-generated.

Distance-Guided Fully Dynamic Docking (DFDD) enables efficient host-guest complexation studies. This unbiased molecular dynamics method captures binding modes and reproduces experimental geometries rapidly, offering a transparent platform for research.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Supramolecular chemistry

Background:

  • Host-guest inclusion complexation is crucial in supramolecular chemistry.
  • Conventional methods like molecular dynamics struggle with stochastic binding events and reproducibility.

Purpose of the Study:

  • To introduce DFDD (Distance-Guided Fully Dynamic Docking), a novel framework for unbiased host-guest inclusion studies.
  • To provide an automated, cloud-ready workflow for efficient complexation analysis.

Main Methods:

  • DFDD utilizes LB-PaCS-MD framework with unbiased molecular dynamics in explicit solvent.
  • Automated workflow includes system setup, parameter generation, iterative MD sampling, and analysis on Google Colab.
  • Host-guest complexation is guided by center-of-mass distance for force-free pathway exploration.

Main Results:

  • DFDD successfully captures both stable and transient host-guest binding modes.
  • The method reproduces experimentally observed inclusion geometries of beta-cyclodextrin within minutes.
  • Intermediate states of guest insertion pathways are revealed.

Conclusions:

  • DFDD offers a transparent, accessible, and efficient platform for host-guest complexation studies.
  • The framework enables rapid and accurate modeling of inclusion processes.
  • Optional integration with pKaNET-Cloud supports pH-aware and stereochemically consistent simulations.