A complete solution for ligand-receptor docking
The Advantages of Computational Docking
The widespread use of combinatorial chemistry and high-throughput screening (HTS) in the pharmaceutical and biotechnology industries means that large numbers of compounds can now routinely be investigated for biological activity. However, screening large chemical libraries remains an expensive and time-consuming process, with significant rates of both false positives and false negatives.
High-speed computational methods can now enrich the fraction of suitable lead candidates in a chemical database, thereby creating the potential to greatly enhance productivity and dramatically reduce drug development costs. With an ever increasing number of drug discovery projects having access to high-resolution crystal structures of their targets, high-performance ligand-receptor docking is the clear computational strategy of choice to augment and accelerate structure-based drug design.
Glide offers the full range of speed vs. accuracy options, from the HTVS (high-throughput virtual screening) mode for efficiently enriching million compound libraries, to the SP (standard precision) mode for reliably docking tens to hundreds of thousands of ligand with high accuracy, to the XP (extra precision) mode where further elimination of false positives is accomplished by more extensive sampling and advanced scoring, resulting in even higher enrichment.
Glide provides a rational workflow for virtual screening from HTVS to SP to XP, enriching the data at every level such that only an order of magnitude fewer compounds need to be studied at the next higher accuracy level.
Accurate binding mode prediction:
Glide reliably finds the correct binding modes for a large set of test cases. It outperforms other docking programs in achieving lower RMS deviations from native co-crystallized structures.
Glide exhibits excellent docking accuracy and high enrichment across a diverse range of receptor types.
Citations and Acknowledgements
Schrödinger Release 2016-4: Glide, Schrödinger, LLC, New York, NY, 2016.
ö Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T., "Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes," J. Med. Chem., 2006, 49, 6177–6196
ö Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening," J. Med. Chem., 2004, 47, 1750–1759
ö Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy," J. Med. Chem., 2004, 47, 1739–1749
ö "Relative Binding Free Energy Calculations Applied to Protein Homology Models"Cappel, D.; Hall, M.L.; Lenselink, E.B.; Beuming, T.; Qi, J.; Bradner, J.; Sherman, W., J. Chem. Inf. Model., 2016, 56 (12), 2388–2400
"Discovery and Structure Activity Relationships of a Highly Selective Butyrylcholinesterase Inhibitor by Structure-Based Virtual Screening"Dighe, S.N.; Deora,G.S.; Mora, E.; Nachon, F.; Chan, S.; Parat, M.; Brazzolotto, X.; Ross, B.P.;, J. Med. Chem., 2016, 59, 7683−7689
ö "A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring"Sirin, S.; Kumar, R.; Martinez, C.; Karmilowicz, M.J.; Ghosh, P.; Abramov, Y.A.; Martin, V.; Sherman, W., J. Chem. Inf. Model., 2014, 54(8), 2334-2346
ö "A structure-based virtual screening approach for discovery of covalently bound ligands"Toledo Warshaviak, D.; Golan, G.; Borrelli, K.W.; Zhu, K.; Kalid, O., J. Chem. Inf. Model, 2014, 54(7), 1941–1950
ö "Docking covalent inhibitors: A parameter free approach to pose prediction and scoring"Zhu, K.; Borrelli, K.W.; Greenwood, J.R.; Day, T.; Abel, R.; Farid, R.S.; Harder, E., J. Chem. Inf. Model., 2014, 54, 1932−1940
Drug-Like Ligand Decoys Set
Schrödinger has made available a set of the ligand decoys used in Glide enrichment studies.
1K Drug-Like Ligand Decoys Set: This collection of ligands was created by selecting 1000 ligands from a one million compound library that were chosen to exhibit "drug-like" properties. Creation and application of the ligand set is presented in the following publications:
Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S, "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy", J. Med. Chem. 2004, 47, 1739-1749.
Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening", J. Med. Chem. 2004, 47, 1750-1759.
Last updated 11/17/2009
Glide Fragment Library
Set of 441 unique small fragments (1-7 ionization/tautomer variants; 6-37 atoms; MW range 32-226) derived from molecules in the medicinal chemistry literature. The set includes a total of 667 fragments with accessible low energy ionization and tautomeric states and metal and state penalties for each compound from Epik. These can be used for fragment docking, core hopping, lead optimization, de novo design, etc.
Last Updated: 06/28/2009