Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

具有特定性质的化合物的合理设计需要理解和快速评估整个化合物空间的分子性质——所有潜在稳定分子的巨大集合。量子力学计算与机器学习相结合的最新进展为探索广泛的化合物空间提供了强大的工具。我们通过讨论基于量子力学的机器学习方法在不同化合物和性质上的发展和应用的关键进展，并概述了未来的挑战，展示了我们对这个令人兴奋和快速发展的领域的看法。我们认为，通过系统地结合严格的物理理论，可以在探索和理解化合物空间方面取得重大进展，