Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

As graph neural networks are becoming more and more powerful and useful in the field of
drug discovery, many pharmaceutical companies are getting interested in utilizing these …

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …

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 …

Accurately identifying potential drug–target interactions (DTIs) is a key step in drug
discovery. Although many related experimental studies have been carried out for identifying …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

The estimation of chemical reaction properties such as activation energies, rates, or yields is
a central topic of computational chemistry. In contrast to molecular properties, where …

Traditional deep learning (DL)-based rolling bearing fault diagnosis methods usually use
signals collected under specific working condition to train the diagnosis models. This may …

Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility,
is highly desirable for rational compound design in chemical and pharmaceutical industries …