Protein–ligand interactions (PLIs) are essential for biochemical functionality and their
identification is crucial for estimating biophysical properties for rational therapeutic design …

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the
modern drug discovery pipeline as it mitigates the cost, time, and resources required to …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

Background Development of new methods for analysis of protein–protein interactions (PPIs)
at molecular and nanometer scales gives insights into intracellular signaling pathways and …

Computational prediction of ligand–target interactions is a crucial part of modern drug
discovery as it helps to bypass high costs and labor demands of in vitro and in vivo …

Graph neural networks (GNNs) are the most promising deep learning models that can
revolutionize non-Euclidean data analysis. However, their full potential is severely curtailed …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …

The assessment of protein–ligand interactions is critical at early stage of drug discovery.
Computational approaches for efficiently predicting such interactions facilitate drug …

Accurate in-silico identification of protein–protein interactions (PPIs) is a long-standing
problem in biology, with important implications in protein function prediction and drug …