Genetic-guided GFlowNets: Advancing in Practical Molecular Optimization Benchmark

The proposed method shows a stateof-the-art score of 16.213, significantly outperforming the reported best score in the benchmark genetic algorithms (e.g., Jensen, 2019). of 15.185, in practical molecular optimization The recent work of Gao et al. (2022a) proposes a practical (PMO), which is an official benchmark for molecular optimization (PMO) benchmark, emphasizing sample-efficient molecular optimization. Remarkably, the importance of sample efficiency in de novo molecular ours exceeds all baselines, including reinforcement optimization for practical applicability. The benchmark is learning, Bayesian optimization, generative reasonable because real-world applications of molecule optimization models, GFlowNets, and genetic algorithms, (e.g., drug discovery) require expensive scoring in 14 out of 23 tasks. Our code is available at processes such as wet lab experiments.