Advances and challenges in deep generative models for de novo molecule generation,Wiley Interdisciplinary Reviews: Computational Molecular Science

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Advances and challenges in deep generative models for de novo molecule generation
Wiley Interdisciplinary Reviews: Computational Molecular Science ( IF 16.8 ) Pub Date : 2018-10-19 , DOI: 10.1002/wcms.1395
Dongyu Xue _{1,

2} , Yukang Gong _{3,

4} , Zhaoyi Yang ₅ , Guohui Chuai _{1,

2} , Sheng Qu _{1,

2} , Aizong Shen ₅ , Jing Yu _{3,

4} , Qi Liu _{1,

2,

4}

Affiliation

The de novo molecule generation problem involves generating novel or modified molecular structures with desirable properties. Taking advantage of the great representation learning ability of deep learning models, deep generative models, which differ from discriminative models in their traditional machine learning approach, provide the possibility of generation of desirable molecules directly. Although deep generative models have been extensively discussed in the machine learning community, a specific investigation of the computational issues related to deep generative models for de novo molecule generation is needed. A concise and insightful discussion of recent advances in applying deep generative models for de novo molecule generation is presented, with particularly emphasizing the most important challenges for successful application of deep generative models in this specific area.

中文翻译：

从头产生分子的深度生成模型的进展与挑战

从头产生分子的问题涉及产生具有所需特性的新颖或修饰的分子结构。利用深度学习模型的强大表示学习能力，与传统机器学习方法中的判别模型不同的深度生成模型提供了直接生成所需分子的可能性。尽管深度生成模型已在机器学习社区中进行了广泛讨论，但仍需要对与从头生成分子的深度生成模型相关的计算问题进行具体研究。本文简要介绍了有关将深度生成模型应用于从头分子生成的最新进展的精辟，有见地的讨论，

更新日期：2018-10-19

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