Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

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 …

Molecular design approaches typically rely on simplified thermodynamic property prediction
models to be computationally tractable. In addition, the simplified prediction methods have to …

The success of deep machine learning in processing of large amounts of data, for example,
in image or voice recognition and generation, raises the possibilities that these tools can …

Chemical creativity in the design of new synthetic chemical entities (NCEs) with drug-like
properties has been the domain of medicinal chemists. Here, we explore the capability of a …

Established molecular machine learning models process individual molecules as inputs to
predict their biological, chemical, or physical properties. However, such algorithms require …

The discovery of new molecules and materials helps expand the horizons of novel and
innovative real-life applications. In pursuit of finding molecules with desired properties …

The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

A key component of automated molecular design is the generation of compound ideas for
subsequent filtering and assessment. Recently deep learning approaches have been …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …