… We have developed a novel deep neural network estimating the binding affinity of ligand–…
of this representation, treating the atoms of both proteins and ligands in the same manner. Our …

… Furthermore, to evaluate the performance of DeepDTAF in predicting protein–ligand binding
affinity, we compare DeepDTAF with three state of the art deep learning models, DeepDTA […

… based deep learning scoring method, we show the generalized advantage and limitation of
the current protein–ligand … clues to overcome those limitations for protein science community. …

… learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative
for protein–ligand … Emergence of more data-hungry deep learning (DL) approaches in …

… Deep learning has been known to learn representations and patterns in complex data forms…
Our aim was to apply deep learning to predict binding affinity of protein-nonpeptide ligand …

… proteinligand complex as a mass-spring system. Their program calculates average distances
for different pairs of ligand-protein … of supervised machine learning to compute the weights …

… of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand
interactions, and as such will have a large impact on protein research, while being readily …

… In this paper, we proposed one novel deep learning model, DLSSAffinity, to predict
protein–ligand binding affinity based on both global sequence information and local structure …

… different elements that constitute the protein. This representation has been used in virtual
molecule synthesis using deep learning prior to our work [9-10]. The drug (or ligand) is also …

… deep learning-based model to predict protein–ligand interaction using the simple formats
of raw protein 1D sequences and 1D ligands … features or complex 3D protein structures. To …