Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

TL;DR

This paper introduces PMO, a benchmark for practical molecular optimization emphasizing sample efficiency, revealing that many current methods underperform under realistic query limits and highlighting areas for future improvement.

Contribution

The paper presents an open-source benchmark for molecular optimization, focusing on sample efficiency and providing a standardized setup for fair comparison of algorithms.

Findings

Most state-of-the-art methods do not outperform older methods under limited queries.

No existing algorithm efficiently solves certain molecular optimization tasks within 10K queries.

Algorithm choice and molecular assembly strategies significantly impact optimization performance.

Abstract

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.