De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promising approach to solve the problem. In this paper, we propose genetic expert-guided learning (GEGL), a simple yet novel framework for training a deep neural network (DNN) to generate highly-rewarding molecules. Our main idea is to design a "genetic expert improvement" procedure, which generates high-quality targets for imitation learning of the DNN. Extensive experiments show that GEGL significantly improves over state-of-the-art methods. For example, GEGL manages to solve the penalized octanol-water partition coefficient optimization with a score of 31.40, while the best-known score in the literature is 27.22. Besides, for the GuacaMol benchmark with 20 tasks, our method achieves the highest score for 19 tasks, in comparison with state-of-the-art methods, and newly obtains the perfect score for three tasks.

中文翻译：

---

用遗传探索指导深度分子优化

从头分子设计试图在化学空间中搜索具有所需特性的分子。最近，深度学习作为解决该问题的一种有前途的方法受到了广泛关注。在本文中，我们提出了遗传专家指导学习 (GEGL)，这是一种简单而新颖的框架，用于训练深度神经网络 (DNN) 以生成高回报分子。我们的主要思想是设计一个“遗传专家改进”程序，为 DNN 的模仿学习生成高质量的目标。大量实验表明，GEGL 显着改善了最先进的方法。例如，GEGL 设法解决了惩罚性辛醇-水分配系数优化问题，得分为 31.40，而文献中最著名的得分为 27.22。此外，对于 20 个任务的 GuacaMol 基准测试，