Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past
decade. Various AI techniques have been used in many drug discovery applications, such …

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

Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

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 …

The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

Deep generative models have been an upsurge in the deep learning community since they
were proposed. These models are designed for generating new synthetic data including …

Graphs are important data representations for describing objects and their relationships,
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …

Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …

The structural design of functional molecules, also called molecular optimization, is an
essential chemical science and engineering task with important applications, such as drug …