Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization (2206.12411v2)

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.

• The paper introduces the PMO benchmark, evaluating 25 molecular design algorithms over 23 tasks to measure sample efficiency.
• The analysis reveals that older methods can outperform newer models under strict oracle call constraints.
• The study emphasizes the potential of model-based strategies and calls for refined approaches to resource-efficient molecular optimization.

An Analysis of Sample Efficiency in Molecular Optimization: Insights from the PMO Benchmark

The task of molecular optimization has become a focal point in the chemical sciences, primarily to advance drug and material discovery. The research paper, "Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization," scrutinizes the current methods in molecular optimization by establishing a benchmark known as PMO. This paper carefully evaluates the sample efficiency of a variety of algorithms to uncover optimization challenges and proposes solutions to improve the use of computational resources.

Overview of Molecular Optimization Challenges

Molecular design is inherently a complex optimization problem that involves balancing multiple properties to achieve structures that are not only biologically active but also stable and synthesizable. The paper identifies a critical gap in ongoing research—many solutions are tested on arbitrarily designed tasks or rely on simple objectives without a focus on sample efficiency, which is a crucial factor for realistic applications. The presented PMO benchmark aims to address these issues by offering a standardized framework to assess the effectiveness of different molecular optimization techniques within a limited oracle budget, emphasizing sample efficiency.

Experimental Setup and Methodological Insights

The PMO benchmark evaluates 25 molecular design algorithms across 23 optimization tasks. These tasks cover a range of objectives, including machine learning predictors for pharmacological activity such as DRD2 and GSK3β, traditional molecular properties like QED, and multiple properties combined as MPOs from datasets like Guacamol. The experiments are constrained to a maximum of 10,000 oracle calls and utilize the AUC (Area Under the Curve) of top-10 average performance against oracle calls as a metric, tracking the effectiveness of algorithms to optimize with fewer resources.

Key Results and Observations

The results from the PMO benchmark highlight several noteworthy findings:

1. Sample Inefficiency: The research demonstrated that none of the existing algorithms were able to efficiently tackle molecular optimization within a low oracle budget, often required in practical scenarios.
2. Reassessment of State-of-the-Art Methods: Older algorithms such as REINVENT and Graph GA, which date back several years, showcased superior performance compared to many newer methods. This suggests that advancements in the field might have been overstated without rigorous comparative benchmarks.
3. Technical Complexity and Model-Based Methods: The paper indicated that model-based methods like MolPAL could offer enhanced sample efficiency, provided the predictive models are robust. However, simpler models like GA+D did not uniformly benefit from model enhancements.
4. SELFIES vs. SMILES: The usage of SELFIES did not consistently outperform SMILES in optimization scenarios, highlighting its limited impact in problems beyond syntactic validity.

Implications for Future Research

The findings from this paper challenge the current trajectory of molecular optimization research by providing a more nuanced understanding of algorithmic performance in practical applications. The standardization provided by PMO is poised to act as a valuable benchmark in differentiating truly effective methods from those that perform well under unrestricted conditions. Researchers should consider focusing on improving sample efficiency and exploring better model-based strategies that can efficiently navigate large chemical spaces. Furthermore, investigating the landscape of oracle functions can further illuminate which algorithmic strategies may excel under specific conditions.

Conclusion

Conclusively, the PMO benchmark presents a pivotal tool in redefining success metrics in molecular optimization. The thorough evaluation presented encourages the development of more resource-efficient algorithms which are essential for adopting computational methods in real-world drug and material discovery workflows. This paper can significantly reshape the direction of future research by emphasizing the importance of adaptability and efficiency over sheer computational power in molecular design challenges.