A new paradigm for applying deep learning to protein–ligand interaction prediction
… native protein–ligand complex is expressed as the negative … protein–ligand pairs in training,
validation and test sets. The … scoring framework for predicting protein–ligand interactions, …
validation and test sets. The … scoring framework for predicting protein–ligand interactions, …
[HTML][HTML] Decoding the protein–ligand interactions using parallel graph neural networks
… negative samples are determined with RMSD. Protease data were largely directed into the
training set … We considered the top-scoring docked pose for each protein–ligand complex in …
training set … We considered the top-scoring docked pose for each protein–ligand complex in …
Computationally predicting binding affinity in protein–ligand complexes: free energy-based simulations and machine learning-based scoring functions
… non-bonded interactions, with the parameters estimated from the experiment data or QM [41]…
over 1.8 million data entries of experimental protein–ligand interaction data mostly from …
over 1.8 million data entries of experimental protein–ligand interaction data mostly from …
DEELIG: A deep learning approach to predict protein-ligand binding affinity
… the degree of protein-ligand interactions and is a useful … -based approach is the negative
natural logarithmic value of Kd … Training of atomic model for 35 epochs achieved MAE score of …
natural logarithmic value of Kd … Training of atomic model for 35 epochs achieved MAE score of …
Protein-ligand interaction graphs: Learning from ligand-shaped 3d interaction graphs to improve binding affinity prediction
… since it was used as the “scoring power” benchmark in the … For training and performance
evaluation, the negative base-… The quality of docked poses was estimated by calculating the …
evaluation, the negative base-… The quality of docked poses was estimated by calculating the …
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
… for new data, a sufficient amount of training data is required. The … model with existing scoring
functions on the same test set. All … sets, whereas the remainder were labeled as negative …
functions on the same test set. All … sets, whereas the remainder were labeled as negative …
ET‐score: Improving Protein‐ligand Binding Affinity Prediction Based on Distance‐weighted Interatomic Contact Features Using Extremely Randomized Trees …
M Rayka, MH Karimi‐Jafari, R Firouzi - Molecular Informatics, 2021 - Wiley Online Library
… of coefficients and parameters that are estimated from … splitting node, is the only parameter
used to fine tune our model. … to develop ET-Score by training it on docking data to evaluate its …
used to fine tune our model. … to develop ET-Score by training it on docking data to evaluate its …
Baseline model for predicting protein–ligand unbinding kinetics through machine learning
N Amangeldiuly, D Karlov… - Journal of Chemical …, 2020 - ACS Publications
… on the Glide scoring function value, if “bad” contacts were not … -Score-based descriptors for
each protein–ligand complex in … By integrating intermediate-state protein–ligand interaction …
each protein–ligand complex in … By integrating intermediate-state protein–ligand interaction …
[HTML][HTML] Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: A review
… the most common data sets encountered in the training and … In this way, protein-ligand
interactions are encoded implicitly … ) from bad (high RMSD) docking poses using CNNs based on …
interactions are encoded implicitly … ) from bad (high RMSD) docking poses using CNNs based on …
[HTML][HTML] AK-score: accurate protein-ligand binding affinity prediction using an ensemble of 3D-convolutional neural networks
… Our model was trained using the 3772 protein-ligand … They approximate protein-ligand
interactions using equations … When the number of parameters is large, the final parameter set …
interactions using equations … When the number of parameters is large, the final parameter set …