[Paper Review] Learning to Dock: Geometric Deep Learning for Predicting Supramolecular Host-Guest Complexes

Zidi Wang, Tao Zhang+8 more|arXiv (Cornell University)|Jan 18, 2026

Supramolecular Chemistry and Complexes0 citations

TL;DR

The paper introduces DeepHostGuest, a geometric deep-learning framework that predicts host-guest binding conformations and enables accurate binding free-energy calculations across diverse supramolecular systems.

ABSTRACT

Predicting non-covalent host-guest recognition remains challenging due to the complex interplay of electrostatics, dispersion, and steric effects, and the limited transferability of existing docking approaches to synthetic supramolecular systems. Here we present DeepHostGuest, a geometric deep-learning framework that learns generalizable recognition principles directly from experimentally resolved host-guest structures. Hosts are encoded as electrostatic surfaces and guests as molecular graphs, enabling transferable learning across diverse supramolecular systems. DeepHostGuest achieves high-accuracy predictions (RMSD $\leq 2$ Angstrom for 80.8% of test cases), substantially outperforming classical docking without case-specific tuning. Notably, the method generalizes beyond its training domain to crystalline sponge systems, accurately capturing the binding of large amphiphilic molecules within metal-organic cages. Beyond predicting binding conformations, the structures generated by DeepHostGuest serve as a reliable basis for accurate binding free-energy calculations. Density Functional Theory (DFT)-calculated affinities correlate well with experiment, enabling structure-property relationships across 876 host-guest complexes spanning 34 host families. Interpretable feature analysis reveals that binding strength arises from a cooperative interplay of host polarity, guest hydrophobicity, and geometric complementarity, with distinct design regimes across supramolecular classes. Together, these results establish data-driven molecular recognition as a practical route to predictive supramolecular design, enabling high-throughput virtual screening and rational optimization of functional host-guest systems.

Motivation & Objective

• Motivate predictive supramolecular design for non-covalent host–guest recognition.
• Develop a transferable learning framework that generalizes across diverse supramolecular systems.
• Encode hosts as electrostatic surfaces and guests as molecular graphs to capture interactions.

Proposed method

• Encode hosts as electrostatic surfaces to capture polarity and charge distributions.
• Encode guests as molecular graphs to represent topology and connectivity.
• Use geometric deep learning to learn recognition principles from experimentally resolved structures.
• Train to predict binding conformations and generate structures suitable for binding-energy calculations.
• Assess transferability to crystalline sponge systems and large amphiphilic guests within metal-organic cages.

Experimental results

Research questions

• RQ1 Can a geometric deep-learning model learn general principles of host–guest recognition from experimental structures?
• RQ2 To what extent does encoding hosts as electrostatic surfaces and guests as graphs enable transferability across diverse supramolecular classes?
• RQ3 How well do generated docked structures support accurate binding free-energy calculations?
• RQ4 Is the method capable of handling crystalline sponge systems and large amphiphilic guests in MOF cages?
• RQ5 What are the interpretable factors contributing to binding strength across different host families?

Key findings

• DeepHostGuest achieves RMSD ≤ 2 Angstrom for 80.8% of test cases.
• Outperforms classical docking without case-specific tuning.
• The method generalizes to crystalline sponge systems and large amphiphilic guests in MOF cages.
• DFT-calculated affinities correlate well with experiment across 876 host–guest complexes spanning 34 host families.
• Interpretable analysis shows binding strength from cooperative host polarity, guest hydrophobicity, and geometric complementarity.