.png){kind=icon}
返回
Molecular Generative Model Based on an Adversarially Regularized Autoencoder
.png){kind=icon}
OA
AI
DOI：10.1021/acs.jcim.9b00694.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep generative models are attracting great attention as a new promising approach for molecular design. A variety of models reported so far are based on either a variational autoencoder (VAE) or a generative adversarial network (GAN), but they have limitations such as low validity and uniqueness. Here, we propose a new type of model based on an adversarially regularized autoencoder (ARAE). It basically uses latent variables like VAE, but the distribution of the latent variables is estimated by adversarial training like in GAN. The latter is intended to avoid both the insufficiently flexible approximation of posterior distribution in VAE and the difficulty in handling discrete variables in GAN. Our benchmark study showed that ARAE indeed outperformed conventional models in terms of validity, uniqueness, and novelty per generated molecule. We also demonstrated a successful conditional generation of drug-like molecules with ARAE for the control of both cases of single and multiple properties. As a potential real-world application, we could generate epidermal growth factor receptor inhibitors sharing the scaffolds of known active molecules while satisfying drug-like conditions simultaneously.
Keyword:
DRUG DISCOVERY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.3
论文数:
9.1K
被引数:
4.0W
机构
暂无机构信息
引用论文
Molecular generative model based on conditional variational autoencoder for de novo molecular designOAAI
OAAI分享收藏分享收藏Heterotopic Bone Formation With the Use of rhBMP2 in Posterior Minimal Access Interbody Fusion.png)
Spine
SpineIF0
PREAI
分享收藏Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules使用数据驱动的分子连续表示的自动化学设计ACS CENTRAL SCIENCEOAAI
ACS CENTRAL SCIENCEIF10.4
OAAI分享收藏Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks使用递归神经网络生成用于药物发现的聚焦分子库ACS CENTRAL SCIENCEOAAI
ACS CENTRAL SCIENCEIF10.4
OAAI分享收藏
查看更多内容