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