NISQ时代数字量子计算机上的资源高效生物分子对接
Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer
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中文总结 AI 辅助
本研究提出混合量子-经典分子对接方法，结合MVWCP与FBE策略，在IBM量子计算机上验证其可行性，为药物设计提供量子优化新途径。
中文摘要 AI 辅助
分子对接是药物发现中的关键计算任务，目标是高效识别配体与靶标受体蛋白之间的最优结合构象。由于可能的结合构型存在组合爆炸，大型柔性分子的对接仍是计算密集型问题，尤其是在规模化场景下。早期研究表明，分子对接可被重新表述为兼容性图上的最大顶点权重团问题（MVWCP），并通过经典方法求解。本研究提出一种混合量子-经典分子对接方法，利用MVWCP形式化方法结合变分全基编码（FBE）策略，该策略可通过布洛赫球矢量高效编码经典二进制变量；我们进一步证明，FBE目标的全局极小值总能被选为纯乘积态，从而为使用酉变分电路进行优化提供严格依据。分子对接问题首先被映射为代价哈密顿量，该代价哈密顿量在变分框架内最小化，通过受随机虚时演化（ITE）启发的热启动及基于梯度的技术优化。最后，我们还在IBM量子计算机上执行了该电路，证明了量子辅助优化在基于结构的药物设计中的可行性，并指出高级编码技术在计算生物学量子优化中的更广泛应用价值。
英文摘要
Molecular docking is a vital computational task in drug discovery, wherein the objective is to efficiently identify optimal binding poses between a ligand and a target receptor protein. Due to the combinatorial explosion of possible binding configurations, docking of large and flexible molecules remains a computationally intensive problem, especially at scale. Early studies have revealed that the molecular docking can be re-cast as a maximum vertex-weighted clique problem (MVWCP) problem on a compatibility graph to be solved classically. In this work, we proposed a hybrid quantum-classical approach for molecular docking leveraging the MVWCP formalism with a variational full-basis encoding (FBE) strategy, which enables efficient encoding of classical binary variables with Bloch sphere vectors. We further prove that a global minimizer of the FBE objective can always be chosen to be a pure product state, thereby providing a rigorous justification for its optimization using a unitary variational circuit. The molecular docking problem is first mapped to a cost Hamiltonian that is minimized within a variational framework, optimized via a randomized imaginary time evolution (ITE)-inspired warm start, and gradient-based techniques. Finally, we also executed the circuit on an IBM quantum computer, underlying the feasibility and of quantum-assisted optimization for structure-based drug design and point towards the broader utility of advanced encoding techniques in quantum optimization for computational biology.
