Improving Molecule Properties Through 2-Stage VAE
Not on the map. Why?
Abstract (arXiv)
Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE model itself suffers from deficiencies such as poor manifold recovery when data lie on low-dimensional manifold embedded in higher dimensional ambient space and they manifest themselves in each applications differently. The consequences of it in drug discovery is somewhat under-explored. In this paper, we study how to improve the similarity of the data generated via VAE and the training dataset by improving manifold recovery via a 2-stage VAE where the second stage VAE is trained on the latent space of the first one. We experimentally evaluated our approach using the ChEMBL dataset as well as a polymer datasets. In both dataset, the 2-stage VAE method is able to improve the property statistics significantly from a pre-existing method.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 6 Dec 2022.
Builds on
23 of 39 references.
References · 23
- 2013Auto-Encoding Variational Bayescited by 4,053
- 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
- 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
- 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
- 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
- 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
- 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 383
- 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
- 2017Grammar Variational Autoencodercited by 285
- 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 275
- 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 208
- 2018Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discoverycited by 194
11 more
- 2019Diagnosing and Enhancing VAE Modelscited by 193
- 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
- 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 172
- 2021E(n) Equivariant Normalizing Flowscited by 165
- 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
- 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 117
- 2018Syntax-Directed Variational Autoencoder for Structured Datacited by 103
- 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
- 2017Application of generative autoencoder in de novo molecular designcited by 32
- 2018Message-passing neural networks for high-throughput polymer screeningcited by 30
- 2021Variational autoencoders in the presence of low-dimensional data: landscape and implicit biascited by 11
Built on by
Citing papers per year: 2023: 1.
1 paper.