用遗传算法直接生成可合成分子，提升药物设计效率
A Genetic Algorithm for Navigating Synthesizable Molecular Spaces
- 基于合成路径的遗传算法，自动生成可合成分子
- 在2D/3D目标下实现高效属性优化，性能达当前最佳
- 适合需要可合成性的药物分子设计场景
受遗传算法有效性及合成可行性在分子设计中重要性的启发，我们提出SynGA——一种直接在合成路径上操作的简单遗传算法。该方法采用定制的交叉与变异算子，显式约束在可合成分子空间内。通过调整适应度函数，SynGA在多种设计任务中表现优异，包括可合成类似物搜索和样本高效属性优化，适用于二维与三维目标。进一步地，将SynGA与基于机器学习的构建块筛选器结合，显著提升性能。在属性优化中，这一组合形成模型驱动的SynGBO，将SynGA与块筛选嵌入贝叶斯优化的内循环。由于SynGA轻量且从结构上保证可合成性，我们期望它不仅能作为强基准，还可作为合成感知工作流中的通用模块。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。