Quantum Algorithm for Structure-Based Virtual Drug Screening Using Classical Force Fields

TL;DR

This paper introduces a quantum algorithm that integrates classical force fields to efficiently evaluate binding energies in virtual drug screening, addressing the combinatorial complexity of molecular configurations.

Contribution

It presents a novel quantum algorithm that combines classical force field models with quantum computing to evaluate multiple protein-ligand configurations simultaneously.

Findings

Potential to evaluate 2^m configurations in a single quantum run

Reformulates binding energy as matrix inner products

Encodes ligand transformations using unitary operations

Abstract

Structure-based virtual screening must address a combinatorial explosion arising from up to 10^60 drug-like molecules, multiple conformations of proteins and ligands, and all possible spatial translations and rotations of ligands within the binding pocket. Although these calculations are inherently parallelizable, their sheer volume remains prohibitive for classical CPU/GPU resources. Quantum computing offers a promising solution: by using n qubits to compute the binding energy of a single protein-ligand pair and m additional qubits to encode different configurations, the algorithm can simultaneously evaluate 2^m combinations in a single quantum execution. To realize this potential, we propose a quantum algorithm that integrates classical force field models to compute electrostatic and van der Waals interactions on discretized grid points. Binding energy calculations are reformulated as matrix-based inner products, while ligand translations and rotations are encoded using unitary operations. This approach circumvents explicit distance calculations and provides a scalable, quantum-enhanced framework for efficient and high-dimensional binding energy estimation in drug discovery.