Optimizing chemical molecules for desired properties lies at the core of drug development. Despite initial successes made by deep generative models and reinforcement learning methods, these methods were mostly limited by the requirement of predefined attribute functions or parallel data with manually pre-compiled pairs of original and optimized molecules. In this paper, for the first time, we formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data through adversarial training strategies. Our model further enables both preservation of molecular contents and optimization of molecular properties through combining auxiliary guided-variational autoencoders and generative flow techniques. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
翻译:最佳化化学分子以获得理想特性是药物发展的核心。尽管深层基因化模型和强化学习方法取得了初步成功,但这些方法大多受到预先定义属性功能或与人工预编原始分子和优化分子对齐的平行数据要求的限制。在本文件中,我们首次将分子优化作为一种风格转移问题,并提出了一种新的基因化模型,通过对抗性培训战略自动了解两组非平行数据的内部差异。我们的模型进一步使得分子内容得以保存,分子特性得以优化,同时通过辅助性指导性变异自动编码器和基因化流动技术相结合。关于两种分子优化任务、毒性修改和合成改进的实验表明,我们的模型大大优于几种最先进的方法。