ET‐score: Improving Protein‐ligand Binding Affinity Prediction Based on Distance‐weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm

2021 ◽  
Author(s):  
Milad Rayka ◽  
Mohammad Hossein Karimi‐Jafari ◽  
Rohoullah Firouzi
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Extremely Randomized Trees ◽  
Distance Weighted ◽  
Interatomic Contact

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 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

scholarly journals Persistent spectral–based machine learning (PerSpect ML) for protein-ligand binding affinity prediction

2021 ◽  
Vol 7 (19) ◽  
pp. eabc5329
Author(s):  
Zhenyu Meng ◽  
Kelin Xia
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Molecular Descriptors ◽  
Biological Data ◽  
Learning Models ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Spectral Models

Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning–based material, chemical, and biological data analysis. Here, we propose persistent spectral–based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

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