Accurately predicting the binding affinities of large sets of protein-ligand complexes
efficiently is a key challenge in computational biomolecular science, with applications in …

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with
deep learning (DL) algorithms playing a crucial role in predicting protein–ligand binding …

In this work, we present graph-convolutional neural networks for the prediction of binding
constants of protein–ligand complexes. We derived the model using multi task learning …

Predicting the binding affinity of protein–ligand complexes is crucial for computer-aided drug
discovery (CADD) and the identification of potential drug candidates. The deep learning …

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 …

This study presents a proof of concept for utilizing Graph Kolmogorov Arnold Networks
(GraphKAN/GKAN) in predicting the binding affinity of small molecules to protein targets …

Accurate in silico prediction of protein–ligand binding affinity is important in the early stages
of drug discovery. Deep learning-based methods exist but have yet to overtake more …

Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided
drug design. We present a universal physics-based scoring function, named SQM2. 20 …

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-
dimensional data to generate computational models to evaluate binding affinity and …

Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …