OPLS4

A revolutionary advance in modern force fields

Having accurate force field parameters is at the heart of obtaining useful molecular structures and relative energies, and yet many current in silico programs employ force fields that are years, if not decades, old, and suffer from lack of sufficient coverage for many common molecular motifs.

The latest version of the force field, OPLS4, builds upon the extensive coverage and high level of accuracy achieved in OPLS3e, by improving the accuracy of functional groups that have presented significant modeling challenges in the past. In particular, charged groups and sulfur containing moieties are  significantly improved with OPLS4. 

Discover how OPLS4 can improve the quality of computational predictions

Accurate parameters for proteins and nucleic acids
In addition to extensive coverage of small molecules, OPLS4 also contains improved parameters for proteins and nucleic acids, leading to marked improvement in structural stabilization during long MD simulations.

Improved conformational analyses
OPLS4’s more accurate description of torsional energies leads to improved conformational analyses, docking poses and binding free energies.

More accurate free energy predictions
OPLS4 produces accurate predictions of solvation free energies and binding free energies, leading to more accurate rank ordering among congeneric series of compounds.

Virtual sites
A single atom-centered charge is often inadequate for describing the true electrostatic nature of a chemical environment. Halogen bonding, chalcogen bonding and lone pairs are particularly poorly represented by atomic partial charges. OPLS4 uses off-centered partial charges, or “virtual sites” to provide a better description of the true electronic environment, leading to improved energetics and structures.

Force field builder
Even with the significantly more comprehensive coverage of OPLS4, there may still be occasions where the molecular motif under study isn’t fully parameterized. The Force Field Builder allows you to easily set up QM calculations to derive the missing parameters yourself.

OPLS4 Improvements
Building upon the success of OPLS3e, the OPLS4 force field brings further refinements and improvements including more accurate modeling of sulfur and charged groups. The respective advances lead to improved accuracy in FEP+ binding affinity predictions.

Citations and Acknowledgements

Schrödinger Release 2022-4: Schrödinger, LLC, New York, NY, 2021.

ö Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G.; Dahlgren, M.; Russell, E.; Von Bargen, C.; Abel, R.; Friesner, R.; Harder, E.; OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space,

"Noncovalent Interactions in the Oxazaborolidine-Catalyzed Enantioselective Mukaiyama Aldol"

Elliot H. E. Farrar and Matthew N. Grayson, The Journal of Organic Chemistry, 2022, 87(15), 10054-10061

· "Plasticity in ligand recognition at somatostatin receptors"

Michael J. Robertson, Justin G. Meyerowitz, Ouliana Panova, Kenneth Borrelli & Georgios Skiniotis , Nature Structural & Molecular Biology, 2022, 29, 210-217

· "Atomistic molecular dynamics in PEO/PMMA blends having the significantly different glass transition temperatures"

Junko Habasaki, The American Ceramic Society, 2022, ,

"Prediction of self-diffusion coefficients of chemically diverse pure liquids by all-atom molecular dynamics simulations"

Hiromi Baba, Ryo Urano, Tetsuro Nagai, Susumu Okazaki, Journal of Computational Chemistry, 2022, 43(28), 1892-1900

"Molecular Dynamics Prediction of the Solubility of Paracetamol in Polyethylene Glycol- Polylactide Copolymer Formulations"

Isaac D. Tegladza and Victus Kordorwu, Journal of Chemistry Studies, 2022, 1(2), 9-16

"Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction"

Elliot H. E. Farrar and Matthew N. Grayson, Chem Sci, 2022, 13, 7594-7603

· "Macromolecular refinement of X-ray and cryo-electron microscopy structures with Phenix / OPLS3e for improved structure and ligand quality"

van Zundert, G.CP; Moriarty, N.W.; Sobolev, O.V.; Adams, P.D.; Borrelli, K.W., Structure, 2021, ,

· "Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations"

Dana Katz, Michael A. DiMattia, Dan Sindhikara, Hubert Li, Nikita Abraham, Abba E. Leffler, Marine Drugs, 2021, 19(7), 367

"OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space"

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.; and Harder E.D., , 2021, ,

"Novel, Self-Assembling Dimeric Inhibitors of Human β Tryptase"

Giardina, S.F.; Werner, D.S.; Pingle, M.; Feinberg, P.B.; Foreman, K.W.; Bergstrom, D.E.; Arnold, L.D.; Barany, F., J. Med. Chem., 2020, 63(6), 3004–3027

"Polarity- and molecular orbital-engineered host materials for stable and efficient blue thermally activated delayed fluorescence"

Ihn, S-G.; Jeong, D.; Kwon, E.; Kim, Sa.; Chung, Y.S.; Sim, M.; Chwae, J.; Koishikawa, Y.; Jeon, S.O.; Kim, J.S.; Kim, J.; Nam, S.; Choi, H.; Kim, Su., Research Square, 2020, Preprint, XXX-XXX

· "OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules"

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., J. Chem. Theory Comput., 2019, 1863–1874,

· "High throughput evaluation of macrocyclization strategies for conformer stabilization"

Sindhikara, D. and Borrelli, K., Nature, Scientific Reports , 2018, 8 (6585), doi:10.1038/s41598-018-24766-5

"Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation"

Pérez-Benito, L.; Keränen, H.; van Vlijmen, H.; Tresadern, G., Nature, Scientific Reports, 2018, 8 (4883), doi:10.1038/s41598-018-23039-5

"Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories"

Ash, J.; Fourches, D., J. Chem. Inf. Model., 2017, 57 (6), 1286–1299

· "Free Energy Perturbation Calculations of the Thermodynamics of Protein Side-Chain Mutations"

Steinbrecher, T.; Abel, R.; Clark, A.; Friesner, R., J. Mol. Biol. , 2017, 429 (7), 923–929

· "Multifaceted Peptide Assisted One-Pot Synthesis of Gold Nanoparticles for Plectin-1 Targeted Gemcitabine Delivery in Pancreatic Cancer"

Krishnendu, P.; Al-suraih, F.; Gonzalez-Rodriguez, G.; Dutta, S.K.; Wang, E.; Kwak, H.S.; Caulfield, T.R.; Coffer, J.L.; Bhattacharya, S., Nanoscale, 2017, 9, 15622-15634

· "Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors"

Kuhn, B.; Tichy, M.; Wang, L.; Robinson, S.; Martin, R.E.; Kuglstatter, A.; Benz, J.; Giroud, M.; Schirmeister, T.; Abel, R.; Diederich, F.; Hert, J., J. Med. Chem., 2017, 60 (6), 2485–2497

· "A New Mixed All-Atom/Coarse-Grained Model: Application to Melittin Aggregation in Aqueous Solution"

Shelley, M.Y.; Selvan, M.E.; Zhao, J.; Babin, V.; Liao, C.; Li, J.; Shelley, J.C, J. Chem. Theory Comput., 2017, 13 (8), 3881–3897

· "Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study"

Keränen, H.; Pérez-Benit, L.; Ciordia, M.; Delgado, F.; Steinbrecher, T.B.; Oehlrich, D.; van Vlijmen, H.; Trabanco, A.A.; Tresadern, G., J. Chem. Theory Comput., 2017, 13, 1439-1453

· "Calculating Water Thermodynamics in the Binding Site of Proteins – Applications of WaterMap to Drug Discovery"

Cappel, D.; Sherman, W.; Beuming, T., Curr Top Med Chem., 2017, 17 (23), 2586-2598

· "Advancing Drug Discovery through Enhanced Free Energy Calculations"

Abel, R.; Wang, L.; Harder, E.D.; Berne, B.J.; Friesner, R.A., Acc. Chem. Res., 2017, 50, 1625-1632

"A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Drug-like Fragments"

Sellers, B.D.; James, N.C.; Gobbi, A., J. Chem. Inf. Model., 2017, 57 (6), 1265–1275

· "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

· "OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins"

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., J. Chem. Theory Comput., 2016, 2 (1), 281–296
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