Mahmoud, Amr H. and Lill, Jonas F. and Lill, Markus A.. (2020) Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix. 2008 (12027).
Full text not available from this repository.
Official URL: https://edoc.unibas.ch/84289/
Downloads: Statistics Overview
Abstract
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly.
Faculties and Departments: | 05 Faculty of Science > Departement Pharmazeutische Wissenschaften > Pharmazie > Computational Pharmacy (Lill) |
---|---|
UniBasel Contributors: | Lill, Markus A. and Abdallah, Amr |
Item Type: | Preprint |
Publisher: | arXiv |
Number of Pages: | 36 |
Note: | Publication type according to Uni Basel Research Database: Discussion paper / Internet publication |
Related URLs: | |
Last Modified: | 23 Aug 2021 07:34 |
Deposited On: | 23 Aug 2021 07:34 |
Repository Staff Only: item control page