Name
Abstract | ||
|---|---|---|
Protein-ligand docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein-ligand docking. Many previously developed docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein-ligand docking program, it outperformed other state-of-the-art docking programs in docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10822-017-0030-9 | Journal of Computer-Aided Molecular Design |
Keywords | Field | DocType |
Molecular docking,Docking scoring function,Decoy discrimination,Energy parameter optimization,Hybrid scoring function | Design elements and principles,Docking (molecular),Lead Finder,Docking (dog),Decoy,Protein–ligand docking,Chemistry,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 7 | 0920-654X |
Citations | PageRank | References |
1 | 0.36 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minkyung Baek | 1 | 3 | 0.74 |
| Woong-Hee Shin | 2 | 59 | 5.51 |
| Hwan Won Chung | 3 | 1 | 0.36 |
| Chaok Seok | 4 | 163 | 15.89 |