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

Highlights•GNN has attracted wide attention from the field of designing drug molecules.•The
applications of GNN in molecule scoring, molecule generation and optimization, and …

Deep supervised learning has achieved great success in the last decade. However, its
defects of heavy dependence on manual labels and vulnerability to attacks have driven …

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

Molecular graph generation is a fundamental problem for drug discovery and has been
attracting growing attention. The problem is challenging since it requires not only generating …

We consider the problem of molecular graph generation using deep models. While graphs
are discrete, most existing methods use continuous latent variables, resulting in inaccurate …

Generating molecular graphs with desired chemical properties driven by deep graph
generative models provides a very promising way to accelerate drug discovery process …

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 …

Deep learning methods applied to chemistry can be used to accelerate the discovery of new
molecules. This work introduces GraphINVENT, a platform developed for graph-based …

The last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …