Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs)
have become the preferred option for generative modeling, with numerous pre-trained …

Deep generative models of molecules have grown immensely in popularity, trained on
relevant datasets, these models are used to search through chemical space. The …

Recent research on artificial intelligence indicates that machine learning algorithms can
auto-generate novel drug-like molecules. Generative models have revolutionized de novo …

Artificial intelligence (AI) is booming. Among various AI approaches, generative models
have received much attention in recent years. Inspired by these successes, researchers are …

Graph generation techniques are increasingly being adopted for drug discovery. Previous
graph generation approaches have utilized relatively small molecular building blocks such …

The adaptive processing of graph data is a long-standing research topic that has been lately
consolidated as a theme of major interest in the deep learning community. The snap …

In the past few years, de novo molecular design has increasingly been using generative
models from the emergent field of Deep Learning, proposing novel compounds that are …