Abstract Knowledge of the underpinnings of cancer initiation, progression and metastasis
has increased exponentially in recent years. Advanced “omics” coupled with machine …

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 …

Protein–ligand docking is an essential tool in structure-based drug design with applications
ranging from virtual high-throughput screening to pose prediction for lead optimization. Most …

Computational methods that operate on three-dimensional molecular structure have the
potential to solve important questions in biology and chemistry. In particular, deep neural …

The accurate modeling and prediction of small molecule properties and bioactivities depend
on the critical choice of molecular representation. Decades of informatics-driven research …

Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were
highlighted for their high accuracy with affordable computational costs. Yet, the models' …

With the recent explosion in the size of libraries available for screening, virtual screening is
positioned to assume a more prominent role in early drug discovery's search for active …

Applying machine learning algorithms to protein–ligand scoring functions has aroused
widespread attention in recent years due to the high predictive accuracy and affordable …

Accurately predicting changes in protein stability due to mutations is important for protein
engineering and for understanding the functional consequences of missense mutations in …

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 …