论文标题
快速流：基于流的分子图生成模型
FastFlows: Flow-Based Models for Molecular Graph Generation
论文作者
论文摘要
我们提出了一个使用基于归一化模型的框架，自我引用的嵌入式字符串以及有效生成小分子的多目标优化。只有100个小分子的初始训练集，FastFrows在几秒钟内就会产生数千种化学有效分子。由于有效的采样，可以根据需要应用子结构过滤器，以消除具有不合理的部分的化合物。我们使用易于计算和学习的指标来实现药物液化，合成可访问性和合成复杂性，我们执行多目标优化，以证明快速流动在高通量虚拟筛选环境中的功能。与自回归分子生成模型相比，我们的模型显着简单，更容易训练，并且可以快速生成和鉴定吸毒，可合成的分子。
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules. With an initial training set of only 100 small molecules, FastFlows generates thousands of chemically valid molecules in seconds. Because of the efficient sampling, substructure filters can be applied as desired to eliminate compounds with unreasonable moieties. Using easily computable and learned metrics for druglikeness, synthetic accessibility, and synthetic complexity, we perform a multi-objective optimization to demonstrate how FastFlows functions in a high-throughput virtual screening context. Our model is significantly simpler and easier to train than autoregressive molecular generative models, and enables fast generation and identification of druglike, synthesizable molecules.
