CovAngelo: A hybrid quantum-classical computing platform for accurate and scalable drug discovery

TL;DR

CovAngelo is a hybrid quantum-classical platform that models chemical reactions in drug discovery with high accuracy, scalability, and integration of quantum hardware, enabling efficient covalent inhibitor design.

Contribution

It introduces a novel multiscale embedding model combining quantum information metrics with quantum chemistry, supporting diverse hardware and demonstrating improved reaction modeling in drug discovery.

Findings

Successfully modeled covalent binding of zanubrutinib to BTK with reduced computational cost.

Validated scalability on GPU clusters and cloud infrastructures.

Achieved potential quantum speedups up to 20x with integrated quantum devices.

Abstract

We present a computational platform for modeling chemical reactions in complex molecular environments, focused on ligand-protein binding in drug discovery. The platform implements our new quantum-in-quantum-in-classical (QM/QM/MM) multiscale embedding model that integrates molecular dynamics with a quantum-information-enhanced density matrix embedding theory and quantum chemistry solvers, including explicit solvent. Quantum-information metrics are utilized to generate entanglement-consistent orbitals, enabling a high-accuracy description of strongly correlated regions. The framework supports multiple computational backends, including multi-CPU, NVIDIA multi-GPU architectures, and quantum hardware (IQM, IonQ, IBM) integrated under CUDA-Q, and is designed for compatibility with future fault-tolerant quantum systems. The new platform's capabilities are demonstrated by modeling covalent docking of zanubrutinib to Bruton's tyrosine kinase via a Michael addition mechanism, computing the full reaction energy profiles and energy barriers at a reduced computational cost relative to existing methods. As a 2nd-generation anticancer agent, zanubrutinib serves as a proof of concept for covalent inhibitor discovery. Accurate first-principles reaction barrier estimations provided by our method can contribute to reducing false positive and negative rates in drug discovery pipelines. Scalability is validated through benchmarks on GPU clusters, cloud-based CPU infrastructures. We demonstrate integration with quantum devices (up to 20 qubits), alongside resource estimates for fault-tolerant quantum computing, indicating potential speedups of up to 20x. Beyond single reactions, the platform supports the construction of reaction networks in chemical metric space, facilitating ligand screening and systematic exploration of reactive pathways.