Improving Molecule Properties Through 2-Stage VAE
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoverySimilar Papers 제목 키워드 기반
Molecule Optimization by Explainable Evolution
Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery. In this paper, we develop a novel algorithm for optimizing molecule properties via an E…
DiversityDrug DiscoveryDe Novo Molecular Generation with Stacked Adversarial Model
Generating novel drug molecules with desired biological properties is a time consuming and complex task. Conditional generative adversarial models have recently been proposed as promising approaches for de novo drug desi…
Drug DesignvalidInterpreting Molecule Generative Models for Interactive Molecule Discovery
Discovering novel molecules with desired properties is crucial for advancing drug discovery and chemical science. Recently deep generative models can synthesize new molecules by sampling random vectors from latent space …
Drug DiscoveryD3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation
Understanding and predicting the diverse conformational states of molecules is crucial for advancing fields such as chemistry, material science, and drug development. Despite significant progress in generative models, ac…
3D Molecule GenerationGoal directed molecule generation using Monte Carlo Tree Search
One challenging and essential task in biochemistry is the generation of novel molecules with desired properties. Novel molecule generation remains a challenge since the molecule space is difficult to navigate through, an…
Navigate