Variational autoencoders (VAEs) are deep latent space generative models that have been
immensely successful in multiple exciting applications in biomedical informatics such as …

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …

Autoencoders are versatile tools in molecular informatics. These unsupervised neural
networks serve diverse tasks such as data-driven molecular representation and constructive …

We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …

Drug discovery is a challenging process with a huge molecular space to be explored and
numerous pharmacological properties to be appropriately considered. Among various drug …

In this paper, a prototypical deep evolutionary learning (DEL) process is proposed to
integrate deep generative model and multi-objective evolutionary computation for molecular …

The computational discovery of novel materials has been one of the main motivations
behind research in theoretical chemistry for several decades. Despite much effort, this is far …

Designing an optimal and desired drug molecule structure is a challenging problem. Most of
the existing solutions/representations reported in the literature for this problem are complex …

Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet
different “fingerprint” encodings of molecular structures return very different similarity values …

For discovering uncharted chemical space of ionic liquids (ILs) for CO 2 dissolution, a
reliable generative framework combining re-balanced variational autoencoder (VAE) …