  Molecule design is a fundamental problem in molecular science and has
critical applications in a variety of areas, such as drug discovery, material
science, etc. However, due to the large searching space, it is impossible for
human experts to enumerate and test all molecules in wet-lab experiments.
Recently, with the rapid development of machine learning methods, especially
generative methods, molecule design has achieved great progress by leveraging
machine learning models to generate candidate molecules. In this paper, we
systematically review the most relevant work in machine learning models for
molecule design. We start with a brief review of the mainstream molecule
featurization and representation methods (including 1D string, 2D graph, and 3D
geometry) and general generative methods (deep generative and combinatorial
optimization methods). Then we summarize all the existing molecule design
problems into several venues according to the problem setup, including input,
output types and goals. Finally, we conclude with the open challenges and point
out future opportunities of machine learning models for molecule design in
real-world applications.
