Computer-aided drug discovery has been around for decades, although the past few years
have seen a tectonic shift towards embracing computational technologies in both academia …

Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

Motivation Gaining structural insights into the protein–protein interactome is essential to
understand biological phenomena and extract knowledge for rational drug design or protein …

In drug design, compound potency prediction is a popular machine learning application.
Graph neural networks (GNNs) predict ligand affinity from graph representations of protein …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

In this study, we introduce an interpretable graph-based deep learning prediction model,
AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

Compound potency prediction is a popular application of machine learning in drug
discovery, for which increasingly complex models are employed. The general aim is the …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …