Ligand Pose Generation via QUBO-Based Hotspot Sampling and Geometric Triplet Matching

TL;DR

This paper introduces a QUBO-based framework for generating and selecting plausible ligand binding poses in protein pockets, significantly improving virtual screening efficiency and accuracy.

Contribution

The novel QUBO formulation discretizes binding sites and aligns ligand geometric contours to generate extensive pose candidates, enhancing pose recovery rates.

Findings

Achieved up to 97.8% recovery for RMSD < 2.0 Å.

Generated between 110 and 600,000 poses per ligand.

Scalable, hardware-flexible approach suitable for large virtual screening.

Abstract

We propose a framework based on Quadratic Unconstrained Binary Optimization (QUBO) for generating plausible ligand binding poses within protein pockets, enabling efficient structure-based virtual screening. The method discretizes the binding site into a grid and solves a QUBO problem to select spatially distributed, energetically favorable grid points. Each ligand is represented by a three-atom geometric contour, which is aligned to the selected grid points through rigid-body transformation, producing from hundreds to hundreds of thousands of candidate poses. Using a benchmark of 169 protein-ligand complexes, we generated an average of 110 to 600000 poses per ligand, depending on QUBO parameters and matching thresholds. Evaluation against crystallographic structures revealed that a larger number of candidates increases the likelihood of recovering near-native poses, with recovery rates reaching 100 percent for root mean square deviation (RMSD) values below 1.0 angstrom and 95.9 percent for RMSD values below 0.6 angstrom. Since the correct binding pose is not known in advance, we apply AutoDock-based scoring to select the most plausible candidates from the generated pool, achieving recovery rates of up to 82.8 percent for RMSD < 2.0 angstrom, 81.7 percent for RMSD < 1.5 angstrom, and 75.2 percent for RMSD < 1.0 angstrom. When poses with misleading scores are excluded, performance improves further, with recovery rates reaching up to 97.8 percent for RMSD < 2.0 angstrom and 1.5 angstrom, and 95.4 percent for RMSD < 1.0 angstrom. This modular and hardware-flexible framework offers a scalable solution for pre-filtering ligands and generating high-quality binding poses before affinity prediction, making it well-suited for large-scale virtual screening pipelines.