Work

Harnessing Quantum and Liquid Neural Networks for Drug Repurposing in Neurology

Year: 2025

Type: book-chapter

Abstract: Drug development for neurological conditions faces high costs, long timelines, and frequent failures. Drug repurposing offers a solution, enhanced by advanced computational methods. Using the Broad Institute’s Drug Repurposing Hub data, we applied Quantum Neural Networks (QNNs) and Liquid Neural Networks (LNNs) to predict repurposing opportunities. Focusing on drugs’ mechanisms, clinical phases, and targets, we adapted data for quantum hardware using IBM’s Qiskit. QNNs achieved strong results (accuracy = 0.85, F1-score = 0.81, AUC-ROC = 0.88), though LNNs performed better (accuracy = 0.89, F1-score = 0.86, AUC-ROC = 0.91). Despite quantum hardware limits, QNNs show promise for drug repurposing, with potential to surpass classical models as technology advances. This study underscores the value of QNNs and LNNs in accelerating neurological drug discovery through hybrid quantum-classical approaches.

Source: Advances in transdisciplinary engineering

DOI: 10.3233/atde250513

Authors: Don Roosan, Tiffany Khou, Saif Nirzhor, Rubayat Khan, Fahmida Hai, Inyene Essien-Aleksi, Andrius Baskys

Institutions: Merrimack College, Western University of Health Sciences, The University of Texas Southwestern Medical Center, University of Nebraska Medical Center

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4413660845

FWCI: 14.43

Cites:

Cited by: 12

Related to: 10