Cancer remains one of the most challenging diseases to treat, with treatments often taking decades to develop while costing millions of dollars in research. The complexity of cancer biology necessitates an understanding of the problem at a quantum level. Over the last decade, the design of cancer treatments has increasingly moved towards in silico methods, with bioinformatics playing a pivotal role. This shift has enabled the analysis of vast amounts of biological data and the simulation of complex biochemical interactions, accelerating the discovery and optimization of new treatments through AI/ML and QMMM-derived force fields. However, these methods often fail to capture strong correlations and weak interactions, which are crucial for understanding cancer cell mechanisms.

This project leverages high-accuracy Quantum Monte Carlo (QMC) simulations as implemented in the QMCPACK code, which has been specifically developed for exascale computing, to explore critical biochemical interactions in cancer therapy. By focusing on strong correlations and weak interactions—areas where traditional methods fall short—QMC aims to provide unprecedented insights into key cancer treatment mechanisms. Combining expertise in QMC methods, high-performance computing, drug design, and dataset development, the project’s interdisciplinary team aims to advance the understanding of critical cancer-related processes and develop more effective treatments.