Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …

The ability to detect and count certain substructures in graphs is important for solving many
tasks on graph-structured data, especially in the contexts of computational chemistry and …

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 …

Interpretability of machine learning models is critical to scientific understanding, AI safety, as
well as debugging. Attribution is one approach to interpretability, which highlights input …

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …

Generative artificial intelligence algorithms have shown to be successful in exploring large
chemical spaces and designing novel and diverse molecules. There has been considerable …

Graph neural networks (GNNs) have been widely used for predicting properties and
discovering structure–property relationships in chemistry and drug discovery. However …

Aim: In the early stages of drug discovery, various experimental and computational methods
are used to measure the specificity of small molecules against a target protein. The …

This paper, which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23,
serves as a comprehensive survey of my research in transfer learning by utilizing …