Advances and challenges in deep generative models for de novo molecule generation. (19th October 2018)

• Record Type: Journal Article
• Authors: Xue, Dongyu; Gong, Yukang; Yang, Zhaoyi; Chuai, Guohui; Qu, Sheng; Shen, Aizong; Yu, Jing; Liu, Qi
• Is Part Of: Wiley interdisciplinary reviews. Volume 9:Number 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-19
• Subjects: deep generative models -- de novo molecule generation; Chemistry, Physical and theoretical -- Periodicals; Cheminformatics -- Periodicals; Biochemistry -- Periodicals; 541.220285
• DOI: 10.1002/wcms.1395
• Languages: English
• ISSNs: 1759-0876

Abstract

Abstract : 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. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Abstract : Distinctive deep generative models open a new path for efficient de novo molecule generation in computational molecular science. The framework of five distinctive deep generative models, for example, VAE‐based, AAE‐based, GAN‐based, RNN‐based, and hybrid models coupling with reinforcement learning are briefly described, providing a visual and general understanding of the underlying rationales of these models for molecule design.