+ Most Popular
Cunninghamia lanceolata plantations in China
Mammalian lairs in paleo ecological studies and palynology
Studies on technological possibilities in utilization of anhydrous milk fat for production of recombined butter-like products
Should right-sided fibroelastomas be operated upon?
Large esophageal lipoma
Apoptosis in the mammalian thymus during normal histogenesis and under various in vitro and in vivo experimental conditions
Poissons characoides nouveaux ou non signales de l'Ilha do Bananal, Bresil
Desensitizing efficacy of Colgate Sensitive Maximum Strength and Fresh Mint Sensodyne dentifrices
Administration of fluid by subcutaneous infusion: revival of a forgotten method
Tundra mosquito control - an impossible dream?
Schizophrenia for primary care providers: how to contribute to the care of a vulnerable patient population
Geochemical pattern analysis; method of describing the Southeastern limestone regional aquifer system
Incidence of low birth weights in a hospital of Mexico City
Graded management intensity of grassland systems for enhancing floristic diversity
Microbiology and biochemistry of cheese and fermented milk
The ember tetra: a new pygmy characid tetra from the Rio das Mortes, Brazil, Hyphessobrycon amandae sp. n. (Pisces, Characoidei)
Risk factors of contrast-induced nephropathy in patients after coronary artery intervention
Renovation of onsite domestic wastewater in a poorly drained soil
Observations of the propagation velocity and formation mechanism of burst fractures caused by gunshot
Systolic blood pressure in a population of infants in the first year of life: the Brompton study
Haematological studies in rats fed with metanil yellow
Studies on pasteurellosis. I. A new species of Pasteurella encountered in chronic fowl cholera
Dormancy breaking and germination of Acacia salicina Lindl. seeds
therapy of lupus nephritis. a two-year prospective study

DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity

DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity

Peerj 7: E7362

ISSN/ISBN: 2167-8359

PMID: 31380152

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein-ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein-ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein-ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein-ligand interface contact information from a large protein-ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (-logKd or -logKi) about 1.6-1.8 and R value around 0.5-0.6, which is better than the autodock vina whose RMSE value is about 2.2-2.4 and R value is 0.42-0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein-ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein-ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method "pafnucy", the advantage and limitation of both methods have provided clues for improving the deep learning based protein-ligand prediction model in the future.

Please choose payment method:

(PDF emailed within 1 workday: $29.90)

Accession: 069214318

Download citation: RISBibTeXText

Related references

DeepDTAF: a deep learning method to predict protein-ligand binding affinity. Briefings in Bioinformatics 22(5), 2021

A point cloud-based deep learning strategy for protein-ligand binding affinity prediction. Briefings in Bioinformatics 23(1), 2022

DEELIG: a Deep Learning Approach to Predict Protein-Ligand Binding Affinity. Bioinformatics and Biology Insights 15: 11779322211030364, 2021

Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction. Ieee/Acm Transactions on Computational Biology and Bioinformatics 19(1): 407-417, 2022

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model. Physical Chemistry Chemical Physics: Pccp 24(17): 10124-10133, 2022

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 34(21): 3666-3674, 2018

SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex. Journal of Molecular Modeling 4(12): 379-394, 1998

CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture. Journal of Bioinformatics and Computational Biology 9(Suppl 1): 1-14, 2011

Cscore: A Simple Yet Effective Scoring Function for Protein Ligand Binding Affinity Prediction using Modified Cmac Learning Architecture. Journal of Bioinformatics and Computational Biology 9(supp 01): 1-14, 2011

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions. Briefings in Bioinformatics 22(3), 2021

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference. Journal of Chemical Information and Modeling 61(4): 1583-1592, 2021

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions. Bmc Bioinformatics 22(1): 542, 2021

Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction. Briefings in Bioinformatics 22(5), 2021

Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction. Science Advances 7(19), 2021

Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. Journal of Computer-Aided Molecular Design 34(8): 817-830, 2020