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Citations

BioLuminate®

  • Sankar, K.; Trainor, K.; Blazer, L.; Adams, J.; Sidhu, S.; Day, T.; Meiering, E.; Maier, J., “A descriptor set for quantitative structure-property relationship prediction in biologics”, Mol Inform, 2022, 41(9), 2100240
  • Tavella, D.; Ouellette, D. R.; Garofalo, R.; Zhu, K.; Xu, J.; Oloo, E. O.; Negron, C.; Ihnat, P.M., “A novel method for in silico assessment of Methionine oxidation risk in monoclonal antibodies: Improvement over the 2-shell model”, PLoS One, 2022, 17(12)
  • Sankar, K.; Krystek, S. R. Jr; Carl, S. M.; Day, T.; Maier, J. K. X., “AggScore: prediction of aggregation-prone regions in proteins based on the distribution of surface patches”, Proteins, 2018, 86(11), 1147-1156
  • Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D., “Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction”, Proteins, 2014, 82(8), 1646-1655
  • Salam, N. K.; Adzhigirey, M.; Sherman, W.; Pearlman, D. A., “Structure-based approach to the prediction of disulfide bonds in proteins”, Protein Eng Des Sel, 2014, 27(10), 365-74
  • Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K. A., “Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes”, PLoS ONE, 2013, 8(12), e82849

Schrödinger Release 2025-1: BioLuminate, Schrödinger, LLC, New York, NY, 2025.

 

 

Canvas

  • Duan, J.; Dixon, S. L.; Lowrie, J. F.; Sherman, W., “Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods”, J. Molec. Graph. Model., 2010, 29, 157-170
  • Sastry, M.; Lowrie, J. F.; Dixon, S. L.; Sherman, W., “Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments”, J. Chem. Inf. Model., 2010, 50, 771–784

Schrödinger Release 2025-1: Canvas, Schrödinger, LLC, New York, NY, 2025.

 

 

ConfGen

Schrödinger Release 2025-1: ConfGen, Schrödinger, LLC, New York, NY, 2025.

 

 

Core Hopping

Schrödinger Release 2025-1: Core Hopping, Schrödinger, LLC, New York, NY, 2025.

 

 

CovDock

  • Zhu, K.; Borrelli, K. W.; Greenwood, J. R.; Day, T.; Abel, R.; Farid, R. S.; Harder, E., “Docking covalent inhibitors: A parameter free approach to pose prediction and scoring”, J. Chem. Inf. Model., 2014, 54, 1932−1940

Schrödinger Release 2025-1: CovDock, Schrödinger, LLC, New York, NY, 2025.
 

 

DeepAutoQSAR

  • Kaplan, Z.; Ehrlich, S.; Leswing, K., “Benchmark study of DeepAutoQSAR, ChemProp, and DeepPurpose on the ADMET subset of the Therapeutic Data Commons”, Full Article
  • Gion, K.; Gattani, S.; Kaplan, Z., “DeepAutoQSAR hardware benchmark”, Full Article

Schrödinger Release 2025-1: DeepAutoQSAR, Schrödinger, LLC, New York, NY, 2025.
 

 

Desmond

Schrödinger Release 2025-1: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2024. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2025.

 

 

Epik

  • ​​Johnston, R. C.; Yao, K.; Kaplan, Z.; Chelliah, M.; Leswing, K.; Seekins, S.; Watts, S.; Calkins, D.; Chief Elk, J.; Jerome, S. V.; Repasky, M. P;. Shelley, J. C., “Epik: pKa and protonation state prediction through machine learning”, J. Chem. Theory Comput. 2023, 19, 2380–2388

Schrödinger Release 2025-1: Epik, Schrödinger, LLC, New York, NY, 2025.

Please note that the pKa and tautomeric databases provided with Epik are copyrighted material, and should not be extracted, reproduced, or used outside of the context of Epik or LigPrep licensed calculations.

 

 

FEP+

  • Ross, G. A., Lu, C., Scarabelli, G.; Albanese, S. K.; Houang, E.; Abel, R.; Harder, E. D.; Wang, L., “The maximal and current accuracy of rigorous protein-ligand binding free energy calculations”, Commun. Chem., 2023, 6(222)
  • Chen, W.; Cui, D.; Jerome, S.; Michino, M.; Lenselink, E.; Huggins, D.; Beautrait, A.; Vendome, A.; Abel, R.; Friesner, R. A.; Wang, L., “Enhancing hit discovery in virtual screening through absolute protein–ligand binding free-energy calculations”, J. Chem. Inf. Model., 2023, 63(10), 3171–3185
  • Abel, R.; Wang, L.; Harder, E. D.; Berne, B. J.; Friesner, R. A., “Advancing drug discovery through enhanced free energy calculations”, Acc. Chem. Res., 2017, 50(7), 1625-1632
  • Kuhn, B.; Tichý, M.; Wang, L.; Robinson, S.; Martin, R. E.; Kuglstatter, A.; Benz, J.; Giroud, M., Schirmeister, T.; Abel, R.; Diederich, F.; Hert, J., “Prospective evaluation of free energy calculations for the prioritization of Cathepsin L Inhibitors”, J. Med. Chem., 2017, 60(6), 2485-2497
  • Yu, H. S.; Deng, Y.; Wu, Y.; Sindhikara, D.; Rask, A. R.; Kimura, T.; Abel, R.; Wang, L., “Accurate and reliable prediction of the binding affinities of macrocycles to their protein targets”, J. Chem Theory Comput., 2017, 13(12), 6290-6300
  • Wang, L.; Deng, Y.; Wu, Y.; Kim, B.; LeBard, D. N.; Wandschneider, D.; Beachy, M.; Friesner, R. A.; Abel, R., “Accurate modeling of scaffold hopping transformations in drug discovery”, J. Chem Theory Comput., 2017, 13(1), 42-54
  • Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A., “OPLS3: A force field providing broad coverage of drug-like small molecules and proteins”, J. Chem. Theory Comput., 2016, 12(1), 281–296
  • Wang, L.; Wu, Y.; Deng, D.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. , “Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field”, J. Am. Chem. Soc., 2015, 137(7), 2695–2703

Schrödinger Release 2025-1: FEP+, Schrödinger, LLC, New York, NY, 2025.

 

 

Force Fields

  • Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G. A.; Dahlgren, M. K.; Russell, E.; Von Bargen, C. D.; Abel, R.; Friesner, R. A.; Harder, E. D., “OPLS4: Improving force field accuracy on challenging regimes of chemical space”, J. Chem. Theory Comput., 2021, 17(7), 4291–4300
  • Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder E. D., “OPLS3e: Extending force field coverage for drug-like small molecules”, J. Chem. Theory Comput., 2015, 15(3), 1863–1874
  • Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A., “OPLS3: A force field providing broad coverage of drug-like small molecules and proteins”, J. Chem. Theory Comput., 2016, 12(1), 281–296
  • Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., “Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field”, J. Chem. Theory Comput., 2010, 6, 1509–1519
  • Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J., “Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids”, J. Am. Chem. Soc., 1996, 118 (45), 11225-11236
  • Jorgensen, W. L.; Tirado-Rives, J., “The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin”, J. Am. Chem. Soc., 1988, 110(6), 1657-1666

Schrödinger Release 2025-1: Force Fields, Schrödinger, LLC, New York, NY, 2025.
 

 

Formulation ML

  • Chew, A.K., Afzal, M.A.F., Kaplan, Z. et al. “Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures”, npj Comput Mater 11, 72 (2025). https://doi.org/10.1038/s41524-025-01552-2

Schrödinger Release 2025-2: Formulation ML, Schrödinger, LLC, New York, NY, 2025.
 

 

Glide

  • Yang, Y; Yao, K; Repasky, M. P.; Leswing, K; Abel, R; Shoichet, B. K.; Jerome, S. V., “Efficient exploration of chemical space with docking and deep learning”, J. Chem. Theory Comput. 2021, 17(11), 7106–7119
  • 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
  • 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
  • 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

Schrödinger Release 2025-1: Glide, Schrödinger, LLC, New York, NY, 2025.

 

 

GlideEM

  • Robertson, M. J.; van Zundert, G. C. P.; Borrelli, K.; Skiniotis, G., “GemSpot: A Pipeline for Robust Modeling of Ligands into CryoEM Maps”, Structure., 2020, 28(6), 707-716

Schrödinger Release 2025-1: GlideEM, Schrödinger, LLC, New York, NY, 2025.

 

 

IFD-MD

  • Miller, E. B.; Murphy, R. B.; Sindhikara, D.; Borrelli, K. W.; Grisewood, M. J.; Ranalli, F., Dixon; S. L., Jerome; S., Boyles, N. A.; Day, T.; Ghanakota, P.; Mondal, S.; Rafi, S. B.; Troast, D. M.; Abel, R.; Friesner, R. A., “Reliable and accurate solution to the induced fit docking problem for protein–ligand binding”, J. Chem. Theory Comput. 2021, 17(4), 2630–2639
  • Xu, T., Zhu, K.; Beautrait, A.; Vendome, J.; Borrelli, K. W.; Abel, R.; Friesner, R. A.; Miller, E. B., “Induced-fit docking enables accurate free energy perturbation calculations in homology models”, J. Chem. Theory Comput. 2022, 18(9), 5710–5724
  • Coskun, D.; Lihan, M.; Rodrigues, J. P. G. L. M.; Vass, M.; Robinson, D.; Friesner, R. A.; Miller, E. B., “Using AlphaFold and experimental structures for the prediction of the structure and binding affinities of GPCR complexes via induced fit docking and free energy perturbation”, J. Chem. Theory Comput. 2023

Schrödinger Release 2025-1: IFD-MD, Schrödinger, LLC, New York, NY, 2025.
 

 

Induced Fit (IFD)

Schrödinger Release 2025-1: Induced Fit Docking protocol; Glide, Schrödinger, LLC, New York, NY, 2024; Prime, Schrödinger, LLC, New York, NY, 2025.

 

 

Jaguar

  • Cao, Y.; Balduf, T.; Beachy, M. D.; Bennett, M. C.; Bochevarov, A. D.; Chien, A; Dub, P. A.; Dyall, K. G.; Furness, J. W.; Halls, M. D.; Hughes, T. F.; Jacobson, L. D.; Kwak, H. S.; Levine, D. S.; Mainz, D. T.; Moore, K. B.; Svensson, M; Videla, P. E.; Watson, M. A.; Friesner R. A., “Quantum chemical package Jaguar: A survey of recent developments and unique features”, J. Chem. Phys. 2024, 161(5), 052502
  • Bochevarov, A. D.; Harder, E.; Hughes, T. F.; Greenwood, J. R.; Braden, D. A.; Philipp, D. M.; Rinaldo, D.; Halls, M. D.; Zhang, J.; Friesner, R. A., “Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences”, Int. J. Quantum Chem., 2013, 113(18), 2110-2142

Schrödinger Release 2025-1: Jaguar, Schrödinger, LLC, New York, NY, 2025.

 

 

Jaguar pKa

  • Bochevarov, A. D.; Watson, M. A.; Greenwood, J. R.; Philipp, D. M., “Multiconformation, density functional theory-based pKa prediction in application to large, flexible organic molecules with diverse functional groups”, J. Chem. Theory Comput., 2016, 12(12), 6001–6019
  • Yu, H. S.; Watson, M. A.; Bochevarov, A. D., “A weighted averaging scheme and a local atomic descriptor for pKa prediction based on density functional theory”, J. Chem. Inf. Mod., 2018, 58, 271–286
  • Klicić, J. J.; Friesner, R. A.; Liu, S.-Y.; Guida, W. C., “Accurate prediction of acidity constants in aqueous solution via density functional theory and self-consistent reaction field methods”, J. Phys. Chem. A, 2002, 106, 1327–1335

Schrödinger Release 2025-1: Jaguar pKa, Schrödinger, LLC, New York, NY, 2025.

 

 

KNIME Extensions

Schrödinger Release 2025-1: KNIME extensions, Schrödinger, LLC, New York, NY, 2025.

 

 

LigPrep

Schrödinger Release 2025-1: LigPrep, Schrödinger, LLC, New York, NY, 2025.
 

 

LiveDesign

Schrödinger Release 2025-1: LiveDesign, Schrödinger, LLC, New York, NY, 2025.

 

 

MacroModel

  • Mohamadi, F.; Richard, N. G.; Guida, W. C.; Liskamp, R.; Lipton, M.; Caufield, C.; Chang, G.; Hendrickson, T.; Still, W. C., “MacroModel – an integrated software system for modeling organic and bioorganic molecules using molecular mechanics”, J. Comput. Chem. 1990, 11, 440–467
  • Watts, K. S.;  Dalal, P.; Tebben, A. J.; Cheney, D. L.; Shelley, J. C. “Macrocycle conformational sampling with MacroModel”, J. Chem. Inf. Model. 2014, 54(10), 2680–2696

Schrödinger Release 2025-1: MacroModel, Schrödinger, LLC, New York, NY, 2025.
 

 

Maestro

Schrödinger Release 2025-1: Maestro, Schrödinger, LLC, New York, NY, 2025.

 

 

Materials Science Suite

Schrödinger Release 2025-1: Materials Science Suite, Schrödinger, LLC, New York, NY, 2025.
 

 

Materials Coarse-Grain

  • Coscia, B. J.; Shelley, J. C.; Browning, A. R.; Sanders, J. M.; Chaudret, R.; Rozot, R.; Léonforte, F.; Halls, M. D.; Luengo, G. S. “Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations”, Phys. Chem. Chem. Phys.  2023, 25, 1768-1780
  • Afzal, M. A. F.; Lehmkemper, K.; Sobich, E.; Hughes, T. F.; Giesen, D. J.; Zhang, T.; Krauter, C. M.; Winget, P.; Degenhardt, M.; Kyeremateng, S. O.; Browning, A. R.; Shelley, J. C. “Molecular-level examination of amorphous solid dispersion dissolution”, Mol. Pharmaceutics, 2021, 18, 11, 3999–4014 

Schrödinger Release 2025-1: Materials Coarse-Grain, Schrödinger, LLC, New York, NY, 2025.
 

 

Materials Penetrant Loading

  • Sanders, J. M.; Misra, M.; Mustard, T. J. L.; Giesen, D. J.; Zhang, T.; Shelley, J.; Halls, M. D.  “Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods”, Carbohydrate Polymers, 2021, 252, 117161

Schrödinger Release 2025-1: Materials Science Penetrant Loading, Schrödinger, LLC, New York, NY, 2025.
 

 

Phase

  • Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A., “PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening. 1. Methodology and preliminary results”, J. Comput. Aided Mol. Des., 2006, 20, 647-671
  • Dixon, S. L.; Smondyrev, A. M.; Rao, S. N., “PHASE: A novel approach to pharmacophore modeling and 3D database searching”, Chem. Biol. Drug Des., 2006, 67, 370-372

Schrödinger Release 2025-1: Phase, Schrödinger, LLC, New York, NY, 2025.

 

 

Phenix/OPLS

  • van Zundert, G. C. P.; Moriarty, N. W.; Sobolev, O. V.; Adams, P. D.; Borrelli, K. W., “Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality”, Structure., 2021, 29(8), 913-921

Schrödinger Release 2025-1: Phenix/OPLS, Schrödinger, LLC, New York, NY, 2025.

 

 

PIPER

  • Chuang, G-Y.; Kozakov, D.; Brenke, R.; Comeau, S. R.; Vajda, S., “DARS (Decoys As the Reference State) potentials for protein-protein docking”, Biophys. J., 2008, 95, 4217-4227
  • Kozakov, D.; Brenke, R.; Comeau, S. R.; Vajda, S., “PIPER: An FFT-based protein docking program with pairwise potentials”, Proteins, 2006, 65, 392-406
  1. https://rosettadesigngroup.com/blog/535/capri-state-of-protein-protein-docking/
  2. https://www.ebi.ac.uk/msd-srv/capri/

 Schrödinger Release 2025-1: PIPER, Schrödinger, LLC, New York, NY, 2025.

 

 

Prime

Schrödinger Release 2025-1: Prime, Schrödinger, LLC, New York, NY, 2025.

 

 

PrimeX

Schrödinger Release 2025-1: PrimeX, Schrödinger, LLC, New York, NY, 2025.

 

 

Protein Preparation Workflow

Schrödinger Release 2025-1: Protein Preparation Workflow; Epik,  Schrödinger, LLC, New York, NY, 2024; Impact, Schrödinger, LLC, New York, NY; Prime, Schrödinger, LLC, New York, NY, 2025.
 

 

PyMOL

For instructions on citing PyMOL, please visit www.pymol.org/citing.

 

 

QikProp

Schrödinger Release 2025-1: QikProp, Schrödinger, LLC, New York, NY, 2025.

 

 

QSite

  • Murphy, R. B.; Philipp, D. M.; Friesner, R. A., “A mixed quantum mechanics/molecular mechanics (QM/MM) method for large-scale modeling of chemistry in protein environments”, J. Comp. Chem., 2000, 21, 1442-1457
  • Philipp, D. M.; Friesner, R. A., “Mixed ab initio QM/MM modeling using frozen orbitals and tests with alanine dipeptide and tetrapeptide”, J. Comp. Chem., 1999, 20, 1468-1494

Schrödinger Release 2025-1: QSite, Schrödinger, LLC, New York, NY, 2025.

 

 

Semiempirical NDDO

Schrödinger Release 2025-1: Semiempirical NDDO protocol; Jaguar, Schrödinger, LLC, New York, NY, 2024; MOPAC, Schrödinger, LLC, New York, NY, 2025.

 

 

Shape Screening

  • Sastry, G. M.; Dixon, S. L.; Sherman, W., “Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring”, J. Chem. Inf. Model., 2011, 51, 2455-2466

Schrödinger Release  2025-1: Phase, Schrödinger, LLC, New York, NY, 2025.

 

 

SiteMap

Schrödinger Release 2025-1: SiteMap, Schrödinger, LLC, New York, NY, 2025.

 

 

WaterMap

Schrödinger Release 2025-1: WaterMap, Schrödinger, LLC, New York, NY, 2025.

 

 

WScore

  • Murphy, R. B.; Repasky, M. P.; Greenwood, J. R.; Tubert-Brohman, I.; Steven Jerome, S.; Annabhimoju, R.; Boyles, N. A.; Schmitz, C. D.; Abel, R.; Farid, R.; and Friesner, R. A., “WScore: A flexible and accurate treatment of explicit water molecules in ligand–receptor docking” Med. Chem. 2016, 59(9), 4364–4384

Schrödinger Release 2025-1: WScore, Schrödinger, LLC, New York, NY, 2025.