  Computer-driven molecular design combines the principles of chemistry,
physics, and artificial intelligence to identify novel chemical compounds and
materials with desired properties for a specific application. In particular,
quantum-mechanical (QM) methods combined with machine learning (ML) techniques
have accelerated the estimation of accurate molecular properties, providing a
direct mapping from 3D molecular structures to their properties. However, the
development of reliable and efficient methodologies to enable \emph{inverse
mapping} in chemical space is a long-standing challenge that has not been
accomplished yet. Here, we address this challenge by demonstrating the
possibility of parametrizing a given chemical space with a finite set of
extensive and intensive QM properties. In doing so, we develop a
proof-of-concept implementation that combines a Variational Auto-Encoder (VAE)
trained on molecular structures with a property encoder designed to learn the
latent representation from a set of QM properties. The result of this joint
architecture is a common latent space representation for both structures and
properties, which enables property-to-structure mapping for small drug-like
molecules contained in the QM7-X dataset. We illustrate the capabilities of our
approach by conditional generation of \emph{de novo} molecular structures with
targeted properties, transition path interpolation for chemical reactions as
well as insights into property-structure relationships. Our findings thus
provide a proof-of-principle demonstration aiming to enable the inverse
property-to-structure design in diverse chemical spaces.
