In AI-driven drug discovery, artificial intelligence has
been widely applied across multiple stages of R&D, including target identification,
virtual screening, molecular generation, and synthesis route planning. However,
in real-world drug design—particularly when addressing first-in-class targets,
difficult-to-drug targets such as pan-mutant KRAS, or other settings where microscopic-level
research information are limited—existing AI methods continue to face clear
limitations. By contrast, quantum computing is based on first-principles
approaches and simulates molecular systems by following the laws of quantum
mechanics at the microscopic scale, providing drug discovery teams with an
alternative technical pathway at the level of fundamental molecular
information.
Quantum computing has demonstrated significant research
value in the biomedical field, yet the transition from technical exploration to
practical application continues to face a critical challenge: the lack of a
unified and comparable evaluation standard. At present, core quantum
algorithms—most notably the variational quantum eigensolver (VQE)—have
primarily been applied to small reference molecular systems, such as H₂, H₂O,
LiH, and BeH₂, or have been limited to case-specific studies involving
individual drug-like compounds. By contrast, there remains no widely adopted
benchmark framework capable of systematically evaluating the performance of
different quantum algorithms and hardware configurations at the scale of
realistic drug-like molecules.
Recently, the world
’
s
first benchmark for quantum computing
–
based
drug design was released. The benchmark was jointly developed by QureGenAI
together with China Pharmaceutical University, Ningbo University of Technology
and Hun-Dun Quantum Experimental Platform of China Mobile (Benchmark link:
). Designed for practical application scenarios
in real world drug discovery, the benchmark provides open-source code and test
datasets and aims to offer a fair and comparable evaluation framework to
support the continued iteration of quantum computing applications in biology
and pharmaceutical industry, as well as for cross-platform comparisons across
different quantum hardware approaches.
The core innovation of the benchmark lies in the
establishment, for the first time, of a standardized evaluation framework that
connects quantum algorithms with realistic drug-like molecules. For drug
discovery researchers, its immediate value lies in being readily usable in
practice and in reducing the need for extensive empirical exploration.
VQE benchmark
workflow of QureGenAI
This work establishes the first systematic VQE benchmarking
framework driven by active space selection. At the methodological level, the
benchmark defines a clear set of evaluation criteria, including heuristic
classification based on chemically grounded metrics , diverse molecular
benchmark suite—such as lovastatin and oseltamivir, employ both UCCSD (unitary
coupled-cluster with singles and doubles) and HEA (hardware-efficient ansatz)
ansatze and it adopts a multi-dimensional evaluation that integrates both
chemistry metrics and architecture metrics. Most importantly, by performing
computations on real superconducting quantum processing unit (QPU), the
benchmark achieves a critical step from simulation-based validation to
empirical evaluation.
The research team performed hardware-level validation on two
superconducting quantum processors with different qubit counts, using three
representative molecules (H₂O, aspirin, and benzene). The experiments evaluated
two superconducting quantum processors with different numbers of qubits, two
basis sets (STO-3G and 6-31G(d)), and a two-qubit hardware-efficient ansatz
(HEA). Across these settings, the resulting converged energy values exhibited
stable convergence behavior. These results are essential for assessing the
practical relevance of quantum computing in drug discovery and provide
empirical evidence supporting hardware–algorithm co-design.
The release of the benchmark addresses key practical
application pain points in quantum drug discovery, providing pharmaceutical
companies and research organizations with technical guidance that can be
directly applied. Based on this benchmark, drug development teams can more
efficiently identify VQE parameter configurations aligned with their specific
needs, thereby reducing repeated trial-and-error costs in parameter selection
and experimental setup.
Similarly, the establishment of the benchmark helps advance
the standardization of technologies in the field of Quantum AIDD, enabling
result comparison and collaboration across different research teams and
companies, and thereby improving the efficiency of technology iteration. In
addition, its open-source design lowers the barrier to applying quantum
computing in drug discovery, allowing more organizations to pursue research
based on a validated benchmark without starting from low-level parameter
exploration.
Overall, as quantum hardware performance continues to
improve and benchmark frameworks of this kind are further refined, quantum
computing is expected to develop closer synergies with AI-driven drug discovery
in the future. Such synergy may, to a certain extent, help alleviate
long-standing challenges in traditional drug discovery—including limitations in
precision and the scarcity of high-quality data—while exploring new technical
pathways that could potentially support gains in drug R&D efficiency.
About QureGenAI
QureGenAI is a drug discovery company operating under a drug
pipeline development and licensing business model, integrating quantum
computing and AI technologies in its R&D approach(aka Quantum AIDD). The
company currently has nine first-in-class (FIC) assets in development,
including two at the Pre-IND stage and four at the PCC stage. Its research
focus spans multiple therapeutic areas, including novel targets for
androgenetic alopecia (AGA), pan-KRAS inhibitors, and HIF-2α agonists.