标题： 通过整合机器学习与量子退火和量子启发优化的蛋白质设计 显示英文标题

标题： Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization

摘要： 蛋白质设计问题涉及找到折叠成给定三维结构的多肽序列。 其严格的算法解法计算上非常耗时，涉及在序列和结构空间中的嵌套搜索。 由于最近的机器学习突破，现在可以绕过结构搜索，这些突破已实现了准确且快速的结构预测。 同样，量子退火机的出现以及所需的新的搜索问题编码可能会彻底改变序列搜索，甚至在经典机器上也能有效执行。 在本工作中，我们引入了一种通用的蛋白质设计方案，其中可以整合机器学习和量子启发算法的算法和技术进步，并迭代学习一个最优的基于物理的评分函数。 在这一首次概念验证应用中，我们将迭代方法应用于一种易于进行全面基准测试的晶格蛋白质模型，发现它能够快速学习基于物理的评分函数并实现有希望的设计性能。 引人注目的是，即使在经典机器上采用，我们的量子启发重写方法也优于传统序列优化。 该方案具有通用性，可以轻松扩展，例如，涵盖非晶格模型，并能整合各种计算平台上的进展，因此代表了蛋白质设计的新范式方法。 显示英文摘要

摘要： The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be easily extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design.