Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …

Enzymes offer a more environmentally friendly and low-impact solution to conventional
chemistry, but they often require additional engineering for their application in industrial …

Virtual screening has emerged as a valuable computational tool for predicting compound–
protein interactions, offering a cost-effective and rapid approach to identifying potential …

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current
state-of-the-art deep learning methods have significantly advanced the field; however, these …

Central in drug design is the identification of biomolecules that uniquely and robustly bind to
a target protein, while minimizing their interactions with others. Accordingly, precise binding …

Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …

Protein engineering is essential for altering the substrate scope, catalytic activity and
selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such …

Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug
development and repositioning. Despite the remarkable progress achieved in recent years …

Many physics-based and machine-learned scoring functions (SFs) used to predict protein-
ligand binding free energies have been trained on the PDBBind dataset. However, it is …

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due
to the limited computational resources in practical applications and is a crucial basis for drug …