CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE

2011 ◽  
Vol 09 (supp01) ◽  
pp. 1-14 ◽  
Author(s):  
XUCHANG OUYANG ◽  
STEPHANUS DANIEL HANDOKO ◽  
CHEE KEONG KWOH
Keyword(s):  
Binding Affinity ◽  
Scoring Function ◽  
Binding Mode ◽  
Computational Method ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Ligand Docking ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

scholarly journals Learning from the ligand: using ligand-based features to improve binding affinity prediction

2019 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett M Morris
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Limited Information ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Abstract Motivation Machine learning scoring functions for protein–ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein–ligand complex, with limited information about the chemical or topological properties of the ligand itself. Results We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest (RF) combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.836, 0.780 and 0.821 on the PDBbind 2007, 2013 and 2016 core sets, respectively, compared to 0.790, 0.746 and 0.814 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a RF using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets. Availability and implementation Data and code to reproduce all the results are freely available at http://opig.stats.ox.ac.uk/resources. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ASFP (AI-based Scoring Function Platform): a web server for the development of customized scoring functions

2020 ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
Qing Ye ◽  
...  
Keyword(s):  
Binding Affinity ◽  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Benchmark Datasets

Abstract Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: 1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein-ligand interactions; 2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; 3) the online prediction module that provides some well-constructed target-specific SFs for VS and a generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.841 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS and binding affinity prediction. Availability and Implementation: ASFP web server is freely available at http://cadd.zju.edu.cn/asfp/.

scholarly journals Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

2019 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett Morris
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Scoring Functions ◽  
Limited Information ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with limited information about the chemical or topological properties of the ligand itself. We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.831, 0.785, and 0.821 on the PDBbind 2007, 2013, and 2016 core sets respectively, compared to 0.790, 0.737, and 0.797 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a Random Forest using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets.<br>

scholarly journals Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

2019 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett Morris
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Scoring Functions ◽  
Limited Information ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with limited information about the chemical or topological properties of the ligand itself. We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.831, 0.785, and 0.821 on the PDBbind 2007, 2013, and 2016 core sets respectively, compared to 0.790, 0.737, and 0.797 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a Random Forest using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets.<br>

Role of polarization in ligand docking and binding affinity prediction for inhibitors of dengue virus

2011 ◽  
Vol 21 (7) ◽  
pp. 1030-1038 ◽  
Author(s):  
Kshatresh Dutta Dubey ◽  
Amit Kumar Chaubey ◽  
Rajendra Prasad Ojha
Keyword(s):  
Dengue Virus ◽  
Binding Affinity ◽  
Ligand Docking ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

scholarly journals DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7362 ◽  
Author(s):  
Haiping Zhang ◽  
Linbu Liao ◽  
Konda Mani Saravanan ◽  
Peng Yin ◽  
Yanjie Wei
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Mean Squared Error ◽  
Scoring Function ◽  
Binding Mode ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Cellular Functions ◽  
Ligand Complex

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.

scholarly journals AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

2020 ◽  
Vol 21 (22) ◽  
pp. 8424
Author(s):  
Yongbeom Kwon ◽  
Woong-Hee Shin ◽  
Junsu Ko ◽  
Juyong Lee
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Complex Structure ◽  
Rational Drug Design ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

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