Network-based prediction of drug combinations with quantum annealing

TL;DR

This paper introduces a quantum annealing algorithm leveraging network medicine principles to predict effective drug combinations, demonstrating promising results in identifying biologically plausible therapies for various diseases.

Contribution

It presents a novel quantum annealing approach based on the principle of Complementary Exposure for predicting drug combinations within a network medicine framework.

Findings

Low-energy configurations match known drug combinations

Algorithm predicts biologically plausible novel combinations

Effective for multiple diseases including Diabetes and Rheumatoid Arthritis

Abstract

The systematic discovery of effective drug combinations is a challenging problem in modern pharmacology, driven by the combinatorial growth of potential pairings and dosage configurations. Network medicine, modeling diseases and drugs as interconnected modules of the human protein-protein interactome, has emerged as a new paradigm for understanding disease mechanisms and drug action. In this work, we propose a quantum annealing-based algorithm for identifying effective drug combinations. Underlying our approach is the biologically motivated principle of `Complementary Exposure', which posits that therapeutic drug combinations target distinct yet complementary regions of a disease module. We translate this into a quadratic unconstrained binary optimisation problem. We test our method for Diabetes Mellitus, Rheumatoid Arthritis, Asthma, and Brain Neoplasms, relying on experimentally validated drug combinations for these diseases. Our simulated quantum annealing experiments reveal that low-energy configurations align with biologically plausible combinations, demonstrating the algorithm's ability to generate novel predictions for drug combinations.