DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
Protein (receptor)--ligand interaction prediction is a critical component in computer-aided
drug design, significantly influencing molecular docking and virtual screening processes …
drug design, significantly influencing molecular docking and virtual screening processes …
A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the
identification and enhancement of molecules that effectively bind to target proteins. Despite …
identification and enhancement of molecules that effectively bind to target proteins. Despite …
[HTML][HTML] PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening
Prediction of protein–ligand interactions (PLI) plays a crucial role in drug discovery as it
guides the identification and optimization of molecules that effectively bind to target proteins …
guides the identification and optimization of molecules that effectively bind to target proteins …
[HTML][HTML] Ligand binding prediction using protein structure graphs and residual graph attention networks
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 …
discovery as it helps to bypass high costs and labor demands of in vitro and in vivo …
Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction
Protein-ligand interactions play a crucial role in various biological functions, with their
accurate prediction being pivotal for drug discovery and design processes. Traditional …
accurate prediction being pivotal for drug discovery and design processes. Traditional …
Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning
D Luo, D Liu, X Qu, L Dong, B Wang - Journal of Chemical …, 2024 - ACS Publications
Improving the generalization ability of scoring functions remains a major challenge in protein–
ligand binding affinity prediction. Many machine learning methods are limited by their …
ligand binding affinity prediction. Many machine learning methods are limited by their …
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction
H Tan, Z Wang, G Hu - Briefings in Bioinformatics, 2024 - academic.oup.com
Protein–ligand interactions are increasingly profiled at high-throughput, playing a vital role
in lead compound discovery and drug optimization. Accurate prediction of binding pose and …
in lead compound discovery and drug optimization. Accurate prediction of binding pose and …
MGPLI: exploring multigranular representations for protein–ligand interaction prediction
Motivation The capability to predict the potential drug binding affinity against a protein target
has always been a fundamental challenge in silico drug discovery. The traditional …
has always been a fundamental challenge in silico drug discovery. The traditional …
A new paradigm for applying deep learning to protein–ligand interaction prediction
Protein–ligand interaction prediction presents a significant challenge in drug design.
Numerous machine learning and deep learning (DL) models have been developed to …
Numerous machine learning and deep learning (DL) models have been developed to …
[HTML][HTML] Multi-PLI: interpretable multi‐task deep learning model for unifying protein–ligand interaction datasets
The assessment of protein–ligand interactions is critical at early stage of drug discovery.
Computational approaches for efficiently predicting such interactions facilitate drug …
Computational approaches for efficiently predicting such interactions facilitate drug …