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Binding_Affinity_by_3D-CNN_ensemble.pdf (1.14 MB)

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using the Ensemble of 3D-Convolutional Neural Network

submitted on 21.03.2020, 00:13 and posted on 23.03.2020, 12:25 by Yongbeom Kwon, Woong-Hee Shin, Junsu Ko, Juyong Lee
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our new model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3D convolutional neural network layers. Our model was trained using the 3740 protein-ligand complexes from the refined set of the PDBbind database and tested using the 270 complexes from the core set. The benchmark results show that the correlation coefficient between the predicted binding affinities by our model and the experimental data is higher than 0.72, which is comparable with the state-of-the-art binding affinity prediction methods. In addition, our method also ranks the relative binding affinities of possible multiple binders of a protein quite accurately. Last, we measured which structural information is critical for predicting binding affinity.


This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2019M3E5D4066897, and 2019M3E5D4066898) This research was also partially supported by Arontier co.


Email Address of Submitting Author


Kangwon National University


Republic of Korea

ORCID For Submitting Author


Declaration of Conflict of Interest


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