-
![[Zoom In]](/images/spacer.gif;jsessionid=267D260A9ACD34C03A735B8F99A0891D)
-
-
Page of 0
|
Prediction of Protein Ligand Binding Affinity Using Deep Networks, M.Sc. Thesis Sharif University of Technology ; Kalhor, Hamid Reza (Supervisor) ; Motahhari, Abolfazl (Co-Supervisor)
Abstract
Protein-ligand binding affinity is extremely important for finding new candidates in drug discovery and computational biochemistry. One of the physical characteristics for protein ligand interactions has been dissociation constant (KD) which can be obtain experimentally. However, there have been tremendous efforts to predict KD using modeling and computational approaches for protein-ligand interactions. In this project, we have exploited Convolutional Neural Network (CNN) model based on KDeep design, PDBBind version 2016 refined set training data, and examining it with KDeep core set test data. In order to modify KDeep,instead of 24 rotations (0, 90, 180 and 270 degrees in selection of two...
Cataloging briefPrediction of Protein Ligand Binding Affinity Using Deep Networks, M.Sc. Thesis Sharif University of Technology ; Kalhor, Hamid Reza (Supervisor) ; Motahhari, Abolfazl (Co-Supervisor)
Abstract
Protein-ligand binding affinity is extremely important for finding new candidates in drug discovery and computational biochemistry. One of the physical characteristics for protein ligand interactions has been dissociation constant (KD) which can be obtain experimentally. However, there have been tremendous efforts to predict KD using modeling and computational approaches for protein-ligand interactions. In this project, we have exploited Convolutional Neural Network (CNN) model based on KDeep design, PDBBind version 2016 refined set training data, and examining it with KDeep core set test data. In order to modify KDeep,instead of 24 rotations (0, 90, 180 and 270 degrees in selection of two...
Find in contentBookmark |
|
|
