Year: 2025
Type: article
Abstract: The convergence of Quantum Computing and Generative Artificial Intelligence (AI) marks a transformative leap in the field of drug discovery. Traditional drug development pipelines are often slow, costly, and limited by classical computational constraints. This paper presents a novel hybrid approach—QuantumGeneraDrug—that integrates quantum generative models with deep learning architectures to accelerate molecule generation, optimize binding affinities, and reduce computation time. A comprehensive literature survey highlights the evolution of both quantum algorithms and generative AI models applied to pharmaceutical research. The proposed system demonstrates improved performance over existing frameworks in key metrics such as valid molecule rate, druglikeness (QED) score, binding affinity accuracy, and processing time per compound. Comparative results are illustrated using numerical tables and visualized via bar and pie charts. The study concludes that Quantum Generative AI holds immense promise for next-generation drug discovery, especially in tackling complex diseases and enabling personalized medicine.
Authors: Dr.C. Krishna Priya Dr.C. Krishna Priya, Nazeer Shaik Nazeer Shaik, Dr. P. Chitralingappa Dr. P. Chitralingappa
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412846641