Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for ...

Article citations More>> Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10 ...

In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these ...

Keywords: redevelopment of traditional medicines, snoRNA therapeutic targets, lung cancer, variational graph autoencoder (VGAE), artificial intelligence (AI) Citation: Wang Z, Chen Y, Ma H, Gao H, Zhu ...

Article Highlight | 3-Nov-2023 Deep variational autoencoder for proteomics mass spectrometry data analysis Research image: Figure 1. Schematic diagram of Dear-DIA. view more Credit: Research ...

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...

To this aim, we construct a graph-based binary variational autoencoder to obtain discrete latent vectors, train a factorization machine as a surrogate model, and optimize it with an Ising machine. In ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...