Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS
By O. Korb, Thomas Stützle, T. Exner. 2009
1,274 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Elizabeth Yuriev
- Paul A. Ramsland
- Maxim Totrov
- Ruben Abagyan
- F. Silva-Jr
- Paula Jofily
- António J. M. Ribeiro
- Marco A. C. Neves
In Molecular Biology
- Elizabeth Yuriev
- Paul A. Ramsland
- Maxim Totrov
- Ruben Abagyan
- F. Silva-Jr
- Paula Jofily
- António J. M. Ribeiro
- Marco A. C. Neves
In Computational Chemistry & Drug Discovery
- Elizabeth Yuriev
- Paul A. Ramsland
- F. Silva-Jr
- Paula Jofily
- António J. M. Ribeiro
- Marco A. C. Neves
- Rui P. P. Neves
- Pedro Alexandrino Fernandes
Citations in the corpus
- Software for molecular docking: a review (2017, methodology): “2007); PLANTS, an ant colony optimization method (Korb et al. 2009); pso@autodock, a velocity adaptive and regenerative constricted PSO method (Namasivayam and Günther 2007); ParaDockS, a parallel docking suite having PSO as the optimization algorithm (Banitt and Wolfson 2011); and FIPSDock, the ful”
- Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, methodology): “Two specifically designed scoring functions (PLANTSCHEMPLP and PLANTSPLP) have also been made available [199].”
- Docking and scoring with ICM: the benchmarking results and strategies for improvement (2012, methodology): “reported with PDB entries 1jje, 1hvy, 1gm8, 1jd0 and 1sq5 using alternative docking and scoring methods [36, 47–49].”
- A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios (2022, methodology): “Here, a scoring function is developed that is based on the previously described PLANTS_ChemPLP scoring function [16], but weighs the well depth by the RF_total value.”
- Virtual screening strategies in drug discovery: a critical review. (2013, methodology): “In this study, the authors performed a SBVS study using the new TbrPDEB1 crystal structure and a customized VS method that combines a docking scoring function (PLANTS) [186] with a protein ligand interaction fingerprint (IFP) [110] scoring method to rank molecular docking poses of 385,000 commercial”
- Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “Other empirical scoring functions are GlideScore (Halgren et al., 2004; Friesner et al., 2006), ChemScore (Eldridge et al., 1997), PLANTSCHEMPLP (Korb et al., 2009).”
- Key Topics in Molecular Docking for Drug Design (2019, background): “Plants Ant Colony Optimisation Empirical Academic License Korb, 2007; Korb, 2009 [34,35]”