Sample Efficiency Matters: A Benchmark for Practical Molecular OptimizationDownload PDF

27 May 2022, 02:52 (modified: 12 Oct 2022, 00:01)NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
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 single-objective (scalar) optimization 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.
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URL: https://github.com/wenhao-gao/mol_opt
License: We used ZINC dataset from Sterling, Teague, and John J. Irwin. “ZINC 15–ligand discovery for everyone.” Journal of chemical information and modeling 55.11 (2015): 2324-2337, is free to use for everyone. Redistribution of significant subsets requires written permission from the authors. Our benchmark is under MIT license.
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