eModel-BDB

If you use eFindSite, please cite the following papers:
1 Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des 27 (6): 551-67.     
2 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (2): 135-50.     
If you use eThread, please cite the following papers:
1 Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE 7 (11): e50200.     
2 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 118.     
If you use eRankPPI, please cite the following papers:
1 Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol 15 (1): 23.     
2 Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics 18 (1): 257.     
If you use eAromatic, please cite the following paper:
1 Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des 91 (2): 380-90.     
If you use CMS, please cite the following paper:
1 Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem 64: 403-13.     
If you use GeauxDockd, please cite the following papers:
1 Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem 36 (27): 2013-26.     
2 Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE 11 (7): e0158898.     
If you use eSynth, please cite the following paper:
1 Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform 8: 14.     
If you use eBoxSize, please cite the following paper:
1 Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform 7 (1): 18.     
If you use the ACCase dataset, please cite the following paper:
1 Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules 19 (4): 4021-45.     
If you use eModel-BDB, please cite the following paper:
1 Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience 7: 1-9.     
If you use eToxPred, please cite the following paper:
1 Pu L, Govindaraj RG, Wu HC, Brylinski M. (2019) DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol 15 (2): e1006718.     
If you use eFindSitePPI, please cite the following papers:
1 Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit 28 (1): 35-48.     
2 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 64-71.     
If you use TOUGH-D1, please cite the following paper:
1 Pu L, Govindaraj RG, Wu HC, Brylinski M. (2019) DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol 15 (2): e1006718.     
If you use TOUGH-D1, please cite the following paper:
1 Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des 91 (2): 380-90.     
If you use TOUGH-M1, please cite the following paper:
1 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (1): 91.     
If you use eMatchSite, please cite the following papers:
1 Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
If you use eMolFrag, please cite the following paper:
1 Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model 57 (4): 627-631.     
If you use eVolver, please cite the following papers:
1 Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 77-88.     
If you use eRepo-ORP, please cite the following papers:
1 Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl 4: 13.     
2 Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol 430 (15): 2266-2273.     
If you use eToxPred, please cite the following paper:
1 Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol 20 (1): 2.     
 

A database of 200,005 comparative models of drug-bound proteins based on 1,391,403 interaction data obtained from the Binding Database.

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