[HTML][HTML] Ssnet: A deep learning approach for protein-ligand interaction prediction
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 …
modern drug discovery pipeline as it mitigates the cost, time, and resources required to …
Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies
Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental
challenge of drug discovery. Deep learning methods have been harnessed for this purpose …
challenge of drug discovery. Deep learning methods have been harnessed for this purpose …
[HTML][HTML] Decoding the protein–ligand interactions using parallel graph neural networks
Protein–ligand interactions (PLIs) are essential for biochemical functionality and their
identification is crucial for estimating biophysical properties for rational therapeutic design …
identification is crucial for estimating biophysical properties for rational therapeutic design …
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] PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Background Predicting protein-ligand binding sites is a fundamental step in understanding
the functional characteristics of proteins, which plays a vital role in elucidating different …
the functional characteristics of proteins, which plays a vital role in elucidating different …
[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
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 …
SGPPI: structure-aware prediction of protein–protein interactions in rigorous conditions with graph convolutional network
Y Huang, S Wuchty, Y Zhou… - Briefings in …, 2023 - academic.oup.com
While deep learning (DL)-based models have emerged as powerful approaches to predict
protein–protein interactions (PPIs), the reliance on explicit similarity measures (eg sequence …
protein–protein interactions (PPIs), the reliance on explicit similarity measures (eg sequence …
[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 …
DeepDTAF: a deep learning method to predict protein–ligand binding affinity
Biomolecular recognition between ligand and protein plays an essential role in drug
discovery and development. However, it is extremely time and resource consuming to …
discovery and development. However, it is extremely time and resource consuming to …