用于分子设计的样本高效生成优化
Sample Efficient Generative Optimization for Molecular Design
AI总结：
研究针对分子设计中搜索化学空间成本高的问题，提出样本高效生成优化（SEGO）框架，通过概率代理模型、生成模型等进行贝叶斯优化，在相关基准测试中大幅提升样本效率，接近由实验反馈驱动的优化。
AI中文摘要：
在药物发现、材料设计和催化中的分子优化需要在严格的评估预算下搜索广阔的化学空间，因为高保真预言机和实验测量成本高昂。因此，优化方法的实际影响取决于其样本效率，即找到优秀候选者所需的评估次数。我们引入了样本高效生成优化（SEGO），这是一种用于对自适应生成的分子进行贝叶斯优化的框架。在SEGO中，概率代理模型对化学空间中命中点的位置形成假设，生成模型被引导在该区域提出候选者，通过采集函数选择最有前途的候选者，并且由此产生的预言机调用既用于锐化代理模型，也用于将生成器锚定在实际奖励中。SEGO在实际分子优化（PMO）基准测试中仅使用其他方法消耗的预言机调用的十分之一就达到了当前最优性能，并且在多参数对接任务中，它在大约现有方法一半的预言机调用中获得了十次命中。这些成果使分子优化更接近由直接实验反馈驱动的活动。
英文摘要：
Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly. The practical impact of an optimization method therefore hinges on its sample efficiency: how few evaluations it needs to find strong candidates. We introduce Sample Efficient Generative Optimization (SEGO), a framework for Bayesian optimization on adaptively generated molecules. In SEGO, a probabilistic surrogate model forms a hypothesis about where hits lie in chemical space, a generative model is steered to propose candidates in that region, the most promising candidate is selected via an acquisition function, and the resulting oracle call is used both to sharpen the surrogate and to anchor the generator in real reward. SEGO attains state-of-the-art performance on the practical molecular optimization (PMO) benchmark using only one tenth of the oracle calls consumed by other methods, and on a multiparameter docking task it reaches ten hits in roughly half the oracle calls of existing approaches. These gains move molecular optimization closer to campaigns driven by direct experimental feedback.
