The discovery of functional molecules is an expensive and time-consuming process,
exemplified by the rising costs of small molecule therapeutic discovery. One class of …

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 …

There has been a wave of generative models for molecules triggered by advances in the
field of Deep Learning. These generative models are often used to optimize chemical …

De novo drug design aims to generate novel chemical compounds with desirable chemical
and pharmacological properties from scratch using computer-based methods. Recently …

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

Generating new molecules with specified chemical and biological properties via generative
models has emerged as a promising direction for drug discovery. However, existing …

Generative models are becoming a tool of choice for exploring the molecular space. These
models learn on a large training dataset and produce novel molecular structures with similar …

Generative machine learning models sample molecules from chemical space without the
need for explicit design rules. To enable the generative design of innovative molecular …

Designing compounds with desired properties is a key element of the drug discovery
process. However, measuring progress in the field has been challenging due to the lack of …

De novo molecule design algorithms often result in chemically unfeasible or synthetically
inaccessible molecules. A natural idea to mitigate this problem is to bias these algorithms …