scholarly journals Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

2018 ◽  
Vol 34 (21) ◽  
pp. 3666-3674 ◽  
Author(s):  
Marta M Stepniewska-Dziubinska ◽  
Piotr Zielenkiewicz ◽  
Pawel Siedlecki
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Learning Model ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Deep Learning Model

scholarly journals DeepPLA: a novel deep learning-based model for protein-ligand binding affinity prediction

2021 ◽  
Author(s):  
Bomin Wei ◽  
Xiang Gong
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
New Drug ◽  
Drug Molecules ◽  
Approved Drugs ◽  
Vector Representations ◽  
Tremendous Challenge

AbstractThe substantial cost of new drug research and development has consistently posed a huge burden and tremendous challenge for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs and identifying novel interactions between the drug molecules and the target proteins based on computational methods have gained growing attention. Here, we propose the DeepPLA, a novel deep learning-based model that combines ResNet-based 1D CNN and biLSTM, to establish an end-to-end network for protein-ligand binding affinity prediction. We first apply pre-trained embedding methods to encode the raw drug molecular SMILES strings and target protein sequences into dense vector representations. The dense vector representations separately go through ResNet-based 1D CNN modules to derive features. The extracted feature vectors are concatenated and further fed into the biLSTM network after average pooling operation, followed by the MLP module to finally predict binding affinity. We used BindingDB dataset for training and evaluating our DeepPLA model. The result shows that the DeepPLA model reaches a good performance for the protein-ligand binding affinity prediction in terms of R, RMSE, MAE, R2 and MSE with 0.89, 0.68, 0.50, 0.79 and 0.46 on the training set; and scores 0.84, 0.80, 0.60, 0.71 and 0.64 on the independent testing set, respectively. This result suggests the high accuracy of the DeepPLA prediction performance, as well as its high capability in generalization, demonstrating that the DeepPLA can be the potential upgrade to pinpoint new drug-target interactions to find better destinations for proven drugs.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

scholarly journals Assessment of programs for ligand binding affinity prediction

2008 ◽  
Vol 29 (8) ◽  
pp. 1316-1331 ◽  
Author(s):  
Ryangguk Kim ◽  
Jeffrey Skolnick
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

scholarly journals Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

2018 ◽  
Vol 33 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Duc Duy Nguyen ◽  
Zixuan Cang ◽  
Kedi Wu ◽  
Menglun Wang ◽  
Yin Cao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Binding Affinity ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Grand Challenges

scholarly journals Pred-binding: large-scale protein–ligand binding affinity prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1443-1450 ◽  
Author(s):  
Piar Ali Shar ◽  
Weiyang Tao ◽  
Shuo Gao ◽  
Chao Huang ◽  
Bohui Li ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Large Scale ◽  
Binding Affinity Prediction ◽  
Affinity Prediction
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