Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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

Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

General conditions for organic reactions are important but rare, and efforts to identify them
usually consider only narrow regions of chemical space. Discovering more general reaction …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Many important tasks in chemistry revolve around molecules during reactions. This requires
predictions far from the equilibrium, while most recent work in machine learning for …

Conspectus Recent advances in computer hardware and software have led to a revolution
in deep neural networks that has impacted fields ranging from language translation to …

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

Structure-based virtual screening is an important tool in early stage drug discovery that
scores the interactions between a target protein and candidate ligands. As virtual libraries …

Uncertainty quantification (UQ) is an important benchmark to assess the performance of
artificial intelligence (AI) and particularly deep learning ensembled-based models. However …