The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
Abstract
A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
Self-citationDevelopment and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Comparative evaluation of 11 scoring functions for molecular docking.
Wang, R., Lu, Y., & Wang, S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0203783 · Cited by 849
A model binding site for testing scoring functions in molecular docking.
Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., & Shoichet, B. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00777-5 · Cited by 237
Cited by
Water as an active constituent in cell biology
Ball, P. (2008) · Chemical Reviews · doi:10.1021/cr068037a · Cited by 2,177
A practical guide to large-scale docking
Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00597-z · Cited by 546
“Visual examination can catch these strained compounds, but various tools have been developed to systematically analyze hundreds or thousands of compounds making them useful in this setting 127 – 129 .”
MethodMoitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Guerrero, L., Castillo, J., Quiñones, M., Garcia-Vallvé, S., Arola, L., Pujadas, G., & Muguerza, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049493 · Cited by 391
Duin, E. C., Wagner, T., Shima, S., Prakash, D., Cronin, B., Yáñez-Ruiz, D. R., Duval, S., Rümbeli, R., Stemmler, R. T., Thauer, R. K., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600298113 · Cited by 339
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
Harriman, G., Greenwood, J., Bhat, S., Huang, X., Wang, R., Paul, D., Tong, L., Saha, A. K., Westlin, W. F., Kapeller, R., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520686113 · Cited by 287
Self-citationMolecular basis for redox control by the human cystine/glutamate antiporter system xc−
Parker, J. L., Deme, J. C., Kolokouris, D., Kuteyi, G., Biggin, P. C., Lea, S. M., & Newstead, S. (2021) · Nature Communications · doi:10.1038/s41467-021-27414-1 · Cited by 272
Chemical modulation of chaperone-mediated autophagy by retinoic acid derivatives
Anguiano, J., Garner, T. P., Mahalingam, M., Das, B. C., Gavathiotis, E., & Cuervo, A. M. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1230 · Cited by 222
How good are AlphaFold models for docking-based virtual screening?
Scardino, V., Di Filippo, J. I., & Cavasotto, C. N. (2023) · iScience · doi:10.1016/j.isci.2022.105920 · Cited by 207
Bauer, M. R., Ibrahim, T. M., Vogel, S. M., & Boeckler, F. M. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400115b · Cited by 200
Slominski, A. T., Kim, T.-K., Li, W., Yi, A.-K., Postlethwaite, A., & Tuckey, R. C. (2014) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2013.10.012 · Cited by 170
Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls
Kumalo, H., Bhakat, S., & Soliman, M. (2015) · Molecules · doi:10.3390/molecules20021984 · Cited by 158
Computational tools to model halogen bonds in medicinal chemistry.
Ford, M. C., & Ho, P. S. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00997 · Cited by 143
Molnar, L. F., He, X., Wang, B., & Merz, K. M. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3173809 · Cited by 123
Kim, B., Wang, Y.-C., Hespen, C. W., Espinosa, J., Salje, J., Rangan, K. J., Oren, D. A., Kang, J. Y., Pedicord, V. A., & Hang, H. C. (2019) · eLife · doi:10.7554/elife.45343 · Cited by 120
Design principles for cyclin K molecular glue degraders
Kozicka, Z., Suchyta, D. J., Focht, V., Kempf, G., Petzold, G., Jentzsch, M., Zou, C., Di Genua, C., Donovan, K. A., Coomar, S., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-023-01409-z · Cited by 113
Ban, F., Dalal, K., Li, H., LeBlanc, E., Rennie, P. S., & Cherkasov, A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00137 · Cited by 109
Modeling the binding of diverse ligands within the Ah receptor ligand binding domain
Giani Tagliabue, S., Faber, S. C., Motta, S., Denison, M. S., & Bonati, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-47138-z · Cited by 106
In silico repurposing of antipsychotic drugs for Alzheimer’s disease
Kumar, S., Chowdhury, S., & Kumar, S. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0394-8 · Cited by 103
Link-INVENT: Generative Linker Design with Reinforcement Learning
Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · doi:10.26434/chemrxiv-2022-qkx9f · Cited by 101
WScore: a flexible and accurate treatment of explicit water molecules in ligand-receptor docking.
Murphy, R. B., Repasky, M. P., Greenwood, J. R., Tubert-Brohman, I., Jerome, S., Annabhimoju, R., Boyles, N. A., Schmitz, C. D., Abel, R., Farid, R., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00131 · Cited by 89
Self-citationNew Aryl Hydrocarbon Receptor Homology Model Targeted To Improve Docking Reliability
Motto, I., Bordogna, A., Soshilov, A. A., Denison, M. S., & Bonati, L. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2001617 · Cited by 80
Biased cytochrome P450-mediated metabolism via small-molecule ligands binding P450 oxidoreductase
Jensen, S. B., Thodberg, S., Parween, S., Moses, M. E., Hansen, C. C., Thomsen, J., Sletfjerding, M. B., Knudsen, C., Del Giudice, R., Lund, P. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22562-w · Cited by 64
Sijbesma, E., Visser, E., Plitzko, K., Thiel, P., Milroy, L.-G., Kaiser, M., Brunsveld, L., & Ottmann, C. (2020) · Nature Communications · doi:10.1038/s41467-020-17741-0 · Cited by 72
Kankanala, J., Kirby, K. A., Liu, F., Miller, L., Nagy, E., Wilson, D. J., Parniak, M. A., Sarafianos, S. G., & Wang, Z. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00465 · Cited by 66
Nishiguchi, G., Mascibroda, L. G., Young, S. M., Caine, E. A., Abdelhamed, S., Kooijman, J. J., Miller, D. J., Das, S., McGowan, K., Mayasundari, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44698-1 · Cited by 57
Berko, E. R., Witek, G. M., Matkar, S., Petrova, Z. O., Wu, M. A., Smith, C. M., Daniels, A., Kalna, J., Kennedy, A., Gostuski, I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38195-0 · Cited by 53
Development of HDAC Inhibitors Exhibiting Therapeutic Potential in T-Cell Prolymphocytic Leukemia
Toutah, K., Nawar, N., Timonen, S., Sorger, H., Raouf, Y. S., Bukhari, S., von Jan, J., Ianevski, A., Gawel, J. M., Olaoye, O. O., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00420 · Cited by 51
Ding, B., Wang, J., Li, N., & Wang, W. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300508m · Cited by 45
Pan, M., Neilson, M. P., Grunfeld, A. M., Cruz, P., Wen, X., Insall, R. H., & Jin, T. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2005754 · Cited by 36
Roth, O., Yechezkel, S., Serero, O., Eliyahu, A., Vints, I., Tzeela, P., Carignano, A., Janacek, D. P., Peters, V., Kessel, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02065-3 · Cited by 39
Deng, N., Hoyte, A., Mansour, Y. E., Mohamed, M. S., Fuchs, J. R., Engelman, A. N., Kvaratskhelia, M., & Levy, R. (2016) · Protein Science · doi:10.1002/pro.2997 · Cited by 36
Saini, M. K., & Sanyal, S. N. (2015) · Nutrition and Cancer · doi:10.1080/01635581.2015.1015743 · Cited by 37
Biological and Computational Evaluation of Resveratrol Inhibitors against Alzheimer's Disease
Koukoulitsa, C., Villalonga-Barber, C., Csonka, R., Alexi, X., Leonis, G., Dellis, D., Hamelink, E., Belda, O., Steele, B. R., Micha-Screttas, M., et al. (2016) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.3109/14756366.2014.1003928 · Cited by 37
Faber, S. C., Soshilov, A. A., Giani Tagliabue, S., Bonati, L., & Denison, M. S. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19092692 · Cited by 36
Wang, L., Tang, J., Huber, A. D., Casey, M. C., Kirby, K. A., Wilson, D. J., Kankanala, J., Xie, J., Parniak, M. A., Sarafianos, S. G., et al. (2018) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2018.07.039 · Cited by 36
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Gu, X., Aranganathan, A., & Tiwary, P. (2024) · eLife · doi:10.7554/elife.99702.3 · Cited by 33
Baselious, F., Hilscher, S., Robaa, D., Barinka, C., Schutkowski, M., & Sippl, W. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25021358 · Cited by 32
Bettini, E., Stahl, S. M., De Martin, S., Mattarei, A., Sgrignani, J., Carignani, C., Nola, S., Locatelli, P., Pappagallo, M., Inturrisi, C. E., et al. (2022) · Pharmaceuticals · doi:10.3390/ph15080997 · Cited by 31
Schmitt, N. D., & Agar, J. N. (2017) · Journal of Mass Spectrometry · doi:10.1002/jms.3953 · Cited by 29
Mahmoud, D. E., Faraag, A. H. I., & Abu El-Wafa, W. M. (2021) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-021-03158-4 · Cited by 26
Mc Larney, B. E., Sonay, A. Y., Apfelbaum, E., Mostafa, N., Monette, S., Goerzen, D., Aguirre, N., Exner, R. M., Habjan, C., Isaac, E., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01248-w · Cited by 28
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00752 · Cited by 28
Discovery and Design of Novel Small Molecule GSK-3 Inhibitors Targeting the Substrate Binding Site
Rippin, I., Khazanov, N., Ben Joseph, S., Kudinov, T., Berent, E., Arciniegas Ruiz, S. M., Marciano, D., Levy, L., Gruzman, A., Senderowitz, H., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21228709 · Cited by 25
A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.
Yang, Z., Liu, Y., Chen, Z., Xu, Z., Shi, J., Chen, K., & Zhu, W. (2015) · Journal of Molecular Modeling · doi:10.1007/s00894-015-2681-6 · Cited by 23
Sako, M., Yasuo, N., & Sekijima, M. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01385 · Cited by 21
Baselious, F., Robaa, D., & Sippl, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107700 · Cited by 20
Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations
Hutchinson, K., Silva, D. B., Bohlke, J., Clausen, C., Thomas, A. A., Bonomi, M., & Schlessinger, A. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.11.001 · Cited by 18
Salama, E. A., Elgammal, Y., Wijeratne, A., Lanman, N. A., Utturkar, S. M., Farhangian, A., Li, J., Meunier, B., Hazbun, T. R., & Seleem, M. N. (2024) · Emerging Microbes & Infections · doi:10.1080/22221751.2024.2322649 · Cited by 16
Scoring Functions for Protein–Ligand Interactions
Sotriffer, C. (2012) · Protein‐Ligand Interactions · doi:10.1002/9783527645947.ch12 · Cited by 16
How accurately can one predict drug binding modes using AlphaFold models?
Karelina, M., Noh, J. J., & Dror, R. O. (2023) · doi:10.7554/elife.89386.1 · Cited by 16
Trudeau, S. J., Hwang, H., Mathur, D., Begum, K., Petrey, D., Murray, D., & Honig, B. (2023) · Protein Science · doi:10.1002/pro.4594 · Cited by 16
Baselious, F., Hilscher, S., Hagemann, S., Tripathee, S., Robaa, D., Barinka, C., Hüttelmaier, S., Schutkowski, M., & Sippl, W. (2024) · Archiv der Pharmazie · doi:10.1002/ardp.202400486 · Cited by 16
Nordquist, E. B., Zhao, M., Kumar, A., & MacKerell, A. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01189 · Cited by 15
Demuth, P., Thibol, L., Lemsch, A., Potlitz, F., Schulig, L., Grathwol, C., Manolikakes, G., Schade, D., Roukos, V., Link, A., et al. (2024) · Cancers · doi:10.3390/cancers16203441 · Cited by 14
Benchmarking reverse docking through AlphaFold2 human proteome
(2024) · doi:10.1002/pro.5167 · Cited by 14
Erdogan, A., Ozhan, Y., Sipahi, H., Gurdal, E. E., Sippl, W., & Koksal, M. (2024) · Archiv der Pharmazie · doi:10.1002/ardp.202400238 · Cited by 9
Decoding selectivity: computational insights into AKR1B1 and AKR1B10 inhibition
Liu, M., Qin, X., Li, J., Jiang, Y., Jiang, J., Guo, J., Xu, H., Wang, Y., Bi, H., & Wang, Z. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp05985e · Cited by 7
Bostancioglu, S. M., & Mutlu, O. (2025) · Parasitology Research · doi:10.1007/s00436-024-08433-5 · Cited by 4
Ulug, M. E., Ikram, S., Sayyah, E., & Durdağı, S. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.142602 · Cited by 3
Structural basis for selective inhibition of human GABA transporter GAT3
Mortensen, J. S., Bavo, F., Jensen, M. H., Pedersen, A. P. S., Storm, J. P., Pape, T., Frølund, B., Wellendorph, P., & Shahsavar, A. (2026) · Nature Communications · doi:10.1038/s41467-026-68479-0 · Cited by 3
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2022) · doi:10.1101/2022.06.01.494157 · Cited by 2
Metadata
- Authors
- Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-09-23
- Volume
- 49
- Issue
- 21
- Pages
- 6177-6196
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2102377211
- Cited by
- 6,851
- References
- 4
- FWCI
- 47.84
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
