一种用于可合成分子空间导航的遗传算法
A Genetic Algorithm for Navigating Synthesizable Molecular Spaces
AI总结：
SynGA是一种用于可合成分子空间导航的遗传算法，通过自定义交叉和变异算子确保合成可行性，并结合机器学习过滤器提升性能，适用于属性优化等任务。
摘要：
受遗传算法的有效性和分子设计中可合成性的重要性启发，我们提出了SynGA，一种直接在合成路线上操作的简单遗传算法。我们的方法具有自定义的交叉和变异算子，这些算子明确地将算法限制在可合成的分子空间内。通过修改适应度函数，我们在多种设计任务中展示了SynGA的有效性，包括可合成的类似物搜索和样本高效的属性优化，适用于2D和3D目标。此外，通过将SynGA与基于机器学习的过滤器结合，该过滤器聚焦于构建块集合，我们提升了SynGA的性能至最先进的水平。对于属性优化，这表现为一种基于模型的变体SynGBO，它在贝叶斯优化的内循环中使用SynGA和构建块过滤。由于SynGA轻量且通过构造强制可合成性，我们的期望是SynGA不仅能作为强大的独立基准线，还能作为一种灵活的模块，未来可以融入更大的合成意识工作流中。
英文摘要：
Inspired by the effectiveness of genetic algorithms and the importance of synthesizability in molecular design, we present SynGA, a simple genetic algorithm that operates directly over synthesis routes. Our method features custom crossover and mutation operators that explicitly constrain it to synthesizable molecular space. By modifying the fitness function, we demonstrate the effectiveness of SynGA on a variety of design tasks, including synthesizable analog search and sample-efficient property optimization, for both 2D and 3D objectives. Furthermore, by coupling SynGA with a machine learning-based filter that focuses the building block set, we boost SynGA to state-of-the-art performance. For property optimization, this manifests as a model-based variant SynGBO, which employs SynGA and block filtering in the inner loop of Bayesian optimization. Since SynGA is lightweight and enforces synthesizability by construction, our hope is that SynGA can not only serve as a strong standalone baseline but also as a versatile module that can be incorporated into larger synthesis-aware workflows in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
