In this paper, a prototypical deep evolutionary learning (DEL) process is proposed to integrate deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate promising novel molecular structures for the next evolutionary generation, and (2) generative model fine-tuning using newly generated high-quality samples. Thus, DEL implements a data-model co-evolution concept which improves both sample population and generative model learning. Experiments on public datasets indicate that the sample population obtained by DEL exhibits improvement on property distributions, and dominates samples generated by other baseline molecular optimization algorithms. Furthermore, comparisons with a range of deep generative models show that DEL is beneficial for improving sample populations.

中文翻译：

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分子设计的深度进化学习


在本文中，提出了一种原型深度进化学习（DEL）过程，将深度生成模型和多目标进化计算集成到分子设计中。我们的方法能够（1）在生成模型的潜在空间而不是结构空间中进行进化操作，为下一代进化生成有前途的新颖分子结构，以及（2）使用新生成的高质量进行生成模型微调样品。因此，DEL 实现了数据模型共同进化的概念，可以改善样本总体和生成模型学习。在公共数据集上的实验表明，DEL 获得的样本群体在属性分布上表现出改进，并且主导了其他基线分子优化算法生成的样本。此外，与一系列深度生成模型的比较表明，DEL 有利于改善样本群体。