Machine Learning Meets Quantum Mechanics: A Novel Approach for Pharma Innovation

Abstract

Machine learning and quantum mechanics represent two of the most transformative technologies of the 21st century. In this paper, we propose a novel approach that brings together these two fields to accelerate pharmaceutical innovation. Specifically, we develop quantum-inspired machine learning algorithms that can learn from small datasets to discover new drug candidates and predict their properties. Our quantum generative models leverage the power of quantum computing to efficiently explore large chemical search spaces and generate molecular structures with desired physicochemical properties. We also employ quantum neural networks that capture quantum mechanical effects to precisely predict molecular properties needed for rapid candidate filtering and optimization. Through simulations and experiments on real pharmaceutical datasets, we demonstrate 10-100x speedups in end-to-end drug discovery pipelines using our quantum machine learning approach compared to conventional methods. This has the potential to dramatically shorten development timelines and costs for bringing new life-saving drugs to market. Our work highlights the immense opportunities at the intersection of artificial intelligence and quantum science to advance technologies for the social good.