Highlights•The ML-based and DL-based approaches for profiling kinase targets are
outlined.•The commonly used datasets and computational tools for kinase profiling …

Accurately predicting the binding affinity between proteins and ligands is crucial in drug
screening and optimization, but it is still a challenge in computer-aided drug design. The …

Recently, pre-trained foundation models have enabled significant advancements in multiple
fields. In molecular machine learning, however, where datasets are often hand-curated, and …

Large-scale protein language models (PLMs), such as the ESM family, have achieved
remarkable performance in various downstream tasks related to protein structure and …

Therapeutic antibodies are an essential and rapidly flourishing drug modality. The binding
specificity between antibodies and antigens is decided by complementarity-determining …

Poly (ethylene terephthalate)(PET) is the most abundant polyester plastic and is causing
serious environmental pollution. Rapid biological depolymerization of PET waste at a large …

The accurate prediction of the effect of amino acid mutations for protein–protein interactions
(PPI) is a crucial task in protein engineering, as it provides insight into the relevant biological …

Inspired by the success of deep learning in predicting static protein structures, researchers
are now actively exploring other deep learning algorithms aimed at predicting the …

Accurate prediction of protein‐ligand binding affinities is an essential challenge in structure‐
based drug design. Despite recent advances in data‐driven methods for affinity prediction …

Molecular surfaces imply fingerprints of interaction patterns between proteins. However, non-
equivalent efforts have been paid to incorporating the abundant protein surface information …