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 Pd

SUBMITTER: Kundu I 

PROVIDER: S-EPMC9079328 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature