Hybrid AI Quantum HPC Framework for Accelerated Breast Cancer Drug Discovery
Abstract
Breast cancer is one of the leading causes of cancer-related deaths, and the emergence of resistant tumors has made the existing drugs less effective. The conventional drug discovery process is a time-consuming, expensive, and less accurate process. In this paper, a hybrid Artificial Intelligence, Quantum Computing, and High-Performance Computing framework for the discovery of effective drugs for breast cancer is proposed. The proposed framework for drug discovery involves the application of AI for ligand screening, Quantum Approximate Optimization Algorithm for ligand selection, and the application of both GPU and Quantum Computing for molecular docking and energy optimization, respectively. The proposed hybrid framework for drug discovery reduces the search space from 442 ligands to just 10 ligands, thereby significantly reducing the computational cost of the conventional method. The proposed method of ligand docking using GPUs results in a speedup of over 2 times compared to CPU, and the optimized ligand results in a binding energy of up to -12.92 kcal/mol with the Estrogen Receptor Alpha protein target.
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