  Molecule optimization is an important problem in chemical discovery and has
been approached using many techniques, including generative modeling,
reinforcement learning, genetic algorithms, and much more. Recent work has also
applied zeroth-order (ZO) optimization, a subset of gradient-free optimization
that solves problems similarly to gradient-based methods, for optimizing latent
vector representations from an autoencoder. In this paper, we study the
effectiveness of various ZO optimization methods for optimizing molecular
objectives, which are characterized by variable smoothness, infrequent optima,
and other challenges. We provide insights on the robustness of various ZO
optimizers in this setting, show the advantages of ZO sign-based gradient
descent (ZO-signGD), discuss how ZO optimization can be used practically in
realistic discovery tasks, and demonstrate the potential effectiveness of ZO
optimization methods on widely used benchmark tasks from the Guacamol suite.
Code is available at: https://github.com/IBM/QMO-bench.
