ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...

1a). Next, Dear-DIA uses a variational autoencoder to extract the peak features of fragment ions and maps the features into Euclidean space, and then clusters the features, with different classes ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

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 ...

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Abstract: Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based ... and the geometric ...

Therefore, CRAG is promising for AI-based molecular design in various chemical ... of variational autoencoder, sidestep hurdles associated with linearization of discrete structures by having a decoder ...

Variational graph autoencoder (VGAE) in molecular graph generation ... In drug discovery, the construction mode of chemical networks and the definition of molecular model structure need to be further ...

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 ...