  Molecular optimization is a fundamental goal in the chemical sciences and is
of central interest to drug and material design. In recent years, significant
progress has been made in solving challenging problems across various aspects
of computational molecular optimizations, emphasizing high validity, diversity,
and, most recently, synthesizability. Despite this progress, many papers report
results on trivial or self-designed tasks, bringing additional challenges to
directly assessing the performance of new methods. Moreover, the sample
efficiency of the optimization--the number of molecules evaluated by the
oracle--is rarely discussed, despite being an essential consideration for
realistic discovery applications.
  To fill this gap, we have created an open-source benchmark for practical
molecular optimization, PMO, to facilitate the transparent and reproducible
evaluation of algorithmic advances in molecular optimization. This paper
thoroughly investigates the performance of 25 molecular design algorithms on 23
tasks with a particular focus on sample efficiency. Our results show that most
"state-of-the-art" methods fail to outperform their predecessors under a
limited oracle budget allowing 10K queries and that no existing algorithm can
efficiently solve certain molecular optimization problems in this setting. We
analyze the influence of the optimization algorithm choices, molecular assembly
strategies, and oracle landscapes on the optimization performance to inform
future algorithm development and benchmarking. PMO provides a standardized
experimental setup to comprehensively evaluate and compare new molecule
optimization methods with existing ones. All code can be found at
https://github.com/wenhao-gao/mol_opt.
