Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

中文翻译：

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药物发现中的分子设计：深度生成模型的全面回顾

自提出以来，深度生成模型一直是深度学习社区的热潮。这些模型旨在通过拟合数据的近似分布来生成新的合成数据，包括图像、视频和文本。在过去的几年里，深度生成模型在药物发现尤其是从头分子设计方面表现出了卓越的性能。在这项研究中，对深度生成模型进行了回顾，以见证药物发现的从头分子设计的最新进展。此外，我们根据计算机中的分子表示将这些模型分为两类。然后详细报道了这两种经典类型的模型，并讨论了优缺点。我们还指出了从头分子设计的深度生成模型当前面临的挑战。