  The optimal design of compounds through manipulating properties at the
molecular level is often the key to considerable scientific advances and
improved process systems performance. This paper highlights key trends,
challenges, and opportunities underpinning the Computer-Aided Molecular Design
(CAMD) problems. A brief review of knowledge-driven property estimation methods
and solution techniques, as well as corresponding CAMD tools and applications,
are first presented. In view of the computational challenges plaguing
knowledge-based methods and techniques, we survey the current state-of-the-art
applications of deep learning to molecular design as a fertile approach towards
overcoming computational limitations and navigating uncharted territories of
the chemical space. The main focus of the survey is given to deep generative
modeling of molecules under various deep learning architectures and different
molecular representations. Further, the importance of benchmarking and
empirical rigor in building deep learning models is spotlighted. The review
article also presents a detailed discussion of the current perspectives and
challenges of knowledge-based and data-driven CAMD and identifies key areas for
future research directions. Special emphasis is on the fertile avenue of hybrid
modeling paradigm, in which deep learning approaches are exploited while
leveraging the accumulated wealth of knowledge-driven CAMD methods and tools.
