The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery

Abstract

Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials.

Figures (15)

• Figure 1: ML/HPC/QC convergence leveraging all type of computing plateforms: from classical Graphics Card Units (GPUs) and Central Processing Units (CPUs) to Quantum Processing Units (QPUs) for simulations in drug design and beyond, from synthetic data (A/B) to system preparation (C) and applications (D/E).
• Figure 2: Detailed, step-by-step workflow followed to implement our protein's hydration pocket hybrid NISQ/classical approach; each step is briefly described in a simple flow diagram, whose components are grouped and attached to one of the five main phases in which we schematically partition the procedure: Input, Problem Formulation, QUBO, Hybrid NISQ/Classical procedure and Output. Pictures and formulas are used to illustrate the most relevant steps. The notation used for the formulas can be found in the original paper presenting the method QP.
• Figure 3: Example of accuracy comparison between: our method performing a quantum optimisation on IBM Heron R3 Pittsburgh (violet) on a simplified protein instance encompassing 123 binary variables (qubits), to meat the current hardware limitations; our method performing the optimisation classically (red), to scale-up (900 and 3974 binary variables, respectively) to reach the required complexity in the Drug Discovery application; two of the existing alternative solutions (green), namely Hydraprot hydraprot and Placevent Placevent); the bar plot represents the percentage of crystal water molecules identified in the binding pocket of the 3be7 drug-protein complex structure (PDB). The results demonstrate how, increasing the QUBO complexity to reach the order of 1000 variables, our approach perform comparably to the neural-network Hydraprot.
• Figure 4: Left: Exact classical (CPLEX) vs QPU optimisation results on a 123 variables/qubit hydration site prediction problem formulated as a QUBO. Top panel shows the incumbent solution (light-blue line) over time compared to the best solution identified using the QPU optimisation on IBM R3 Pittsburgh (dashed red line). Bottom panel shows the optimality gap over time, during the 3h optimisation run. Qp2. Right: scaling and resources estimation performed on a protein complex, varying QUBO complexity; the number of 2-qubits gates is plot against the number of QUBO variables/qubits. Qp2
• Figure 5: Pople-style diagram illustrating the exponential wall encountered in classical electronic-structure methods, and the potential mitigation offered by quantum computing approaches. The surface color and elevation qualitatively represent the computational complexity.