학술논문

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations
Document Type
Original Paper
Source
Journal of Computer-Aided Molecular Design: Incorporating Perspectives in Drug Discovery and Design. :1-13
Subject
Molecular dynamic
Machine learning
Computational drug design
D3R
Drug Design Data Resource
Language
English
ISSN
0920-654X
1573-4951
Abstract
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.