Issue 22, 2018

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

Abstract

There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge. We have tried to implement Machine Learning (ML) algorithm models on the protein–ligand binding affinity data already available to predict the binding affinity of the unknown. ML methods are appreciably faster and cheaper as compared to traditional experimental methods or computational scoring approaches. The prerequisites of this prediction are sufficient and unbiased features of training data and a prediction model which can fit the data well. In our study, we have applied Random forest and Gaussian process regression algorithms from the Weka package on protein–ligand binding affinity, which encompasses protein and ligand binding information from PdbBind database. The models are trained on the basis of selective fundamental information of both proteins and ligand, which can be effortlessly fetched from online databases or can be calculated with the availability of structure. The assessment of the models was made on the basis of correlation coefficient (R2) and root mean square error (RMSE). The Random forest model gave R2 and RMSE of 0.76 and 1.31 respectively. We have also used our features and prediction models on the dataset used by others and found that our model with our features outperformed the existing ones.

Graphical abstract: A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

Article information

Article type
Paper
Submitted
01 jan 2018
Accepted
13 mar 2018
First published
28 mar 2018
This article is Open Access
Creative Commons BY license

RSC Adv., 2018,8, 12127-12137

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

I. Kundu, G. Paul and R. Banerjee, RSC Adv., 2018, 8, 12127 DOI: 10.1039/C8RA00003D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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