论文标题
通过基因探索引导深层优化
Guiding Deep Molecular Optimization with Genetic Exploration
论文作者
论文摘要
从头分子设计试图在化学空间上寻找具有所需特性的分子。最近，深度学习已成为解决该问题的一种有希望的方法。在本文中，我们提出了遗传专家指导学习（GEGL），这是一个简单而新颖的框架，用于训练深神网络（DNN）生成高度奖励分子。我们的主要思想是设计一个“遗传专家改进”程序，该程序为模仿DNN学习产生了高质量的目标。广泛的实验表明，GEGL对最新方法显着改善。例如，GEGL设法以31.40的成绩解决了受惩罚的辛醇 - 水分系数优化，而文献中最著名的得分为27.22。此外，对于具有20个任务的鳄梨调查基准，与最先进的方法相比，我们的方法获得了19个任务的最高分数，并且新获得了三个任务的完美分数。
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.
