用联合自提升方法高效优化分子，减少实验次数。
Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
- 联合生成与预测模型，缓解分布偏移问题。
- 在有限评估预算下，性能超越现有最优方法。
- 适合药物设计等需要少样本优化的场景。
生成式分子优化旨在设计性能优于现有化合物的分子，但这类候选分子稀少且评估成本高，因此样本效率至关重要。此外，为预测分子性质而引入的代理模型，在优化过程中因候选分子不断偏离分布而产生分布偏移。为此，本文提出联合自提升方法，包含（i）联合生成-预测模型和（ii）自提升采样机制。前者使生成器与代理模型对齐，缓解分布偏移；后者利用预测部分在推理时引导生成部分，高效生成优化分子。在离线与在线分子优化基准测试中，该方法在有限评估预算下均优于现有最先进方法。
原文摘要 · Abstract (English)
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yielding sample efficiency essential. Additionally, surrogate models introduced to predict molecule evaluations, suffer from distribution shift as optimization drives candidates increasingly out-of-distribution. To address these challenges, we introduce Joint Self-Improvement, which benefits from (i) a joint generative-predictive model and (ii) a self-improving sampling scheme. The former aligns the generator with the surrogate, alleviating distribution shift, while the latter biases the generative part of the joint model using the predictive one to efficiently generate optimized molecules at inference-time. Experiments across offline and online molecular optimization benchmarks demonstrate that Joint Self-Improvement outperforms state-of-the-art methods under limited evaluation budgets.
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