The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

The premise for the great advancement of molecular machine learning is dependent on a
considerable amount of labeled data. In many real-world scenarios the labeled molecules …

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major
histocompatibility complex molecules (MHC) is fundamental to the immune response …

Molecular prediction tasks normally demand a series of professional experiments to label
the target molecule, which suffers from the limited labeled data problem. One of the …

Self-training semi-supervised classification has grown in popularity as a research topic.
However, when faced with several challenges including outliers, imbalanced class, and …

Drug discovery has always been a crucial task for society, and molecular property prediction
is one of the fundamental problem. It is responsible for identifying the target properties or …

Computer-aided drug discovery mainly relies on modern computers to model drug
molecules, which can speed up the process of drug discovery and reduce costs. In this …

CHAPTER 2 PROTEIN ENSEMBLE LEARNING WITH ATROUS SPATIAL PYRAMID
NETWORKS FOR SECONDARY STRUCTURE PREDICTION This chapter investigates the …

Graph Neural Networks (GNNs) are increasingly popular in processing graph-structured
data, yet they face significant challenges when training and testing distributions diverge …