FET:Medium: Drug discovery using quantum machine learning

Existing drug discovery pipelines take 10-15 years from initial idea to market approval and cost billions of dollars. Extensive time is attributed to the expansive search space and lack of efficient search tools, whereas the cost is primarily attributed to inferior quality drug candidates that fail in clinical trials. High-quality search tools are required to increase the variety and quality of drug candidates that enter optimization. While high-performance computing assisted by Artificial Intelligence (AI) can screen a large pool of chemical compounds quickly to narrow down candidates that possess various desirable properties, a very large fraction of potential space for candidate drugs still goes unexplored. Furthermore, it is computationally expensive and inefficient in sampling the desired probability distributions in solution space which grows exponentially with the number of molecules. Quantum AI is more expressive, i.e., it can model a target probability distribution even with a limited number of qubits and parameters to sample from the unexplored regions of the search space. However, their true potential and application in drug discovery remain unexplored. This project will fill this void by creating Quantum Machine Learning (QML) models that will employ noisy quantum computers. If successful, this project will unleash new computational capabilities in discovery applications, e.g., by selecting novel lead chemical compounds versus important target proteins to treat diseases, such as cancer, by converging multiple disciplines. The generic and extendible QML toolset will enable the use of quantum computing for other discovery applications, e.g., material discovery. This project will advance quantum computing and quantum AI by addressing the scalability issue. It will develop an integrated introduction to quantum computing and application for K-12 teachers, including a professional development workshop and curricular materials that address local and national-level standards in science and engineering education. It will also develop undergraduate coursework supported by Penn State Quantum Minor program to prepare a quantum-ready workforce. Researchers will develop DrugVAE (a quantum variational autoencoder) to search and screen ligands and QDock (a quantum docking engine) to validate the ligands and aid in screening. Various scalability, application-level parallelization and training approaches for distributed computing will also be developed. Researchers will optimize and parallelize, map, and schedule the QML workloads from DrugVAE and QDock into target quantum computers considering architectural and hardware constraints for performance, resilience and cost. The output features will be provided to the classical neural network as needed. Researchers will computationally validate QML-generated compounds against slower, traditional docking as well as experimentally determined binding affinities. The research will provide materials for workforce development and undergraduate curriculum. Various tasks will be synergized through novel techniques, such as QML-specific optimization, target-specific search and refinement of model parameters, and optimization based on validation results. This project will cover all levels of abstractions to meet the end goal of drug discovery, e.g., program/circuit design, optimization, circuit-to-architecture mapping, parallelization, and scheduling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.