增强记忆：利用经验回放加速从头分子设计
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
AI总结：
本文提出增强记忆算法，将数据增强与经验回放结合以重复利用预言机评分，显著提升从头分子设计的样本效率，并在PMO基准的23项任务中19项上取得新最优。
摘要：
样本效率是从头分子设计中的一个根本性挑战。理想情况下，分子生成模型应能在最少的预言机评估（计算预测或湿实验）下学会满足期望目标。当使用的预言机能够提供更高的预测精度但带来显著成本时，这一问题变得更加突出。因此，这些预言机无法在实际预算下被直接优化。分子生成模型在与强化学习结合时已展现出卓越的样本效率，这一点已在实用分子优化（PMO）基准中得到证明。在此，我们提出一种名为增强记忆（Augmented Memory）的新算法，该算法将数据增强与经验回放相结合。我们表明，从预言机调用中获得的分数可以被重复用于多次更新模型。我们将增强记忆与先前提出的算法进行比较，并在一项利用任务和一项需要探索与利用的药物发现案例研究中展示出显著提升的样本效率。我们的方法在强制执行计算预算的PMO基准中取得了新的最优结果，在19/23项任务上超越了此前表现最佳的方法。
英文摘要：
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Consequently, these oracles cannot be directly optimized under a practical budget. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, outperforming the previous best performing method on 19/23 tasks.
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