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Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
Abstract
Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
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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
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698
Cited by
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-citationMolecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,344
Identifying and Characterizing Binding Sites and Assessing Druggability
Halgren, T. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800324m · Cited by 1,759
Self-citationBenchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318
Improved Docking of Polypeptides with Glide
Tubert-Brohman, I., Sherman, W., Repasky, M., & Beuming, T. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400128m · Cited by 1,308
Ultra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998
Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
Truchon, J.-F., & Bayly, C. I. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600426e · Cited by 874
DOCK 6: Impact of new features and current docking performance
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 833
Combining docking and molecular dynamic simulations in drug design
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006) · Medicinal Research Reviews · doi:10.1002/med.20067 · Cited by 759
Use of an Induced Fit Receptor Structure in Virtual Screening
Sherman, W., Beard, H. S., & Farid, R. (2006) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2005.00327.x · Cited by 697
Self-citationValidation of Molecular Docking Programs for Virtual Screening against Dihydropteroate Synthase
Hevener, K. E., Zhao, W., Ball, D. M., Babaoglu, K., Qi, J., White, S. W., & Lee, R. E. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800293n · Cited by 668
Salmaso, V., & Moro, S. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00923 · Cited by 666
Virtual ligand screening: strategies, perspectives and limitations
Klebe, G. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.05.012 · Cited by 664
Bikadi, Z., & Hazai, E. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-15 · Cited by 657
Advances and Challenges in Protein-Ligand Docking
Huang, S.-Y., & Zou, X. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11083016 · Cited by 639
Ramírez, D., & Caballero, J. (2018) · Molecules · doi:10.3390/molecules23051038 · Cited by 624
Use of molecular docking computational tools in drug discovery
(2021) · Progress in Medicinal Chemistry · doi:10.1016/bs.pmch.2021.01.004 · Cited by 621
Jain, A. N. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9114-2 · Cited by 587
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
Sun, H., Li, Y., Shen, M., Tian, S., Xu, L., Pan, P., Guan, Y., & Hou, T. (2014) · Phys. Chem. Chem. Phys. · doi:10.1039/c4cp03179b · Cited by 526
Farid, R., Day, T., Friesner, R. A., & Pearlstein, R. A. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2005.12.032 · Cited by 502
Self-citationFlavonoids for Controlling Starch Digestion: Structural Requirements for Inhibiting Human α-Amylase
Lo Piparo, E., Scheib, H., Frei, N., Williamson, G., Grigorov, M., & Chou, C. J. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm800115x · Cited by 472
Virtual screening strategies in drug discovery
McInnes, C. (2007) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2007.08.033 · Cited by 469
Variability in docking success rates due to dataset preparation.
Corbeil, C. R., Williams, C. I., & Labute, P. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9570-1 · Cited by 469
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. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500118s · Cited by 462
Bell, E. W., & Zhang, Y. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0362-7 · Cited by 462
ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers
Watts, K. S., Dalal, P., Murphy, R. B., Sherman, W., Friesner, R. A., & Shelley, J. C. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100015j · Cited by 440
Self-citationDevelopment and validation of a modular, extensible docking program: DOCK 5
Moustakas, D. T., Lang, P. T., Pegg, S., Pettersen, E., Kuntz, I. D., Brooijmans, N., & Rizzo, R. C. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9060-4 · Cited by 440
Comparison of Topological, Shape, and Docking Methods in Virtual Screening
McGaughey, G. B., Sheridan, R. P., Bayly, C. I., Culberson, J. C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.-F., & Cornell, W. D. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700052x · Cited by 417
Virtual screening and repurposing of FDA approved drugs against COVID-19 main protease
Kandeel, M., & Al-Nazawi, M. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117627 · Cited by 412
Validation Studies of the Site-Directed Docking Program LibDock
Rao, S. N., Head, M. S., Kulkarni, A., & LaLonde, J. M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6004299 · Cited by 408
In silico studies on therapeutic agents for COVID-19: Drug repurposing approach
Shah, B., Modi, P., & Sagar, S. R. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117652 · Cited by 401
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
CRISPR-Cpf1 assisted genome editing of Corynebacterium glutamicum
Jiang, Y., Qian, F., Yang, J., Liu, Y., Dong, F., Xu, C., Sun, B., Chen, B., Xu, X., Li, Y., et al. (2017) · Nature Communications · doi:10.1038/ncomms15179 · Cited by 389
Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 388
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
Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
Wang, C., & Zhang, Y. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24667 · Cited by 334
Integrating Molecular Docking and Molecular Dynamics Simulations
Santos, L. H. S., Ferreira, R. S., & Caffarena, E. R. (2019) · Methods in Molecular Biology · doi:10.1007/978-1-4939-9752-7_2 · Cited by 285
L-2-Hydroxyglutarate production arises from noncanonical enzyme function at acidic pH
Intlekofer, A. M., Wang, B., Liu, H., Shah, H., Carmona-Fontaine, C., Rustenburg, A. S., Salah, S., Gunner, M. R., Chodera, J. D., Cross, J. R., et al. (2017) · Nature Chemical Biology · doi:10.1038/nchembio.2307 · Cited by 268
Amikacin: Uses, Resistance, and Prospects for Inhibition
Ramirez, M., & Tolmasky, M. (2017) · Molecules · doi:10.3390/molecules22122267 · Cited by 265
Grinter, S., & Zou, X. (2014) · Molecules · doi:10.3390/molecules190710150 · Cited by 260
An artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
Durrant, J. D., & McCammon, J. A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100244v · Cited by 259
Structure and Inhibition of the SARS Coronavirus Envelope Protein Ion Channel
Pervushin, K., Tan, E., Parthasarathy, K., Lin, X., Jiang, F. L., Yu, D., Vararattanavech, A., Soong, T. W., Liu, D. X., & Torres, J. (2009) · PLoS Pathogens · doi:10.1371/journal.ppat.1000511 · Cited by 253
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
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
Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy
Li, C., Deng, X., Xie, X., Liu, Y., Friedmann Angeli, J. P., & Lai, L. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01120 · Cited by 183
Human Glucocorticoid Receptor β Binds RU-486 and Is TranscriptionallyActive
Lewis-Tuffin, L. J., Jewell, C. M., Bienstock, R. J., Collins, J. B., & Cidlowski, J. A. (2007) · Molecular and Cellular Biology · doi:10.1128/mcb.01439-06 · Cited by 175
Carabet, L. A., Rennie, P. S., & Cherkasov, A. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms20010120 · Cited by 172
Wang, Q., Liberti, M. V., Liu, P., Deng, X., Liu, Y., Locasale, J. W., & Lai, L. (2017) · Cell Chemical Biology · doi:10.1016/j.chembiol.2016.11.013 · Cited by 147
Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery
(2023) · doi:10.1021/acs.jcim.2c01219 · Cited by 136
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133
Fragment-based covalent ligand discovery
Lu, W., Kostic, M., Zhang, T., Che, J., Patricelli, M. P., Jones, L. H., Chouchani, E. T., & Gray, N. S. (2021) · RSC Chemical Biology · doi:10.1039/d0cb00222d · Cited by 136
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
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
Rovelet-Lecrux, A., Charbonnier, C., Wallon, D., Nicolas, G., Seaman, M. N. J., Pottier, C., Breusegem, S. Y., Mathur, P. P., Jenardhanan, P., Le Guennec, K., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.100 · Cited by 106
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
Systematic exploitation of multiple receptor conformations for virtual ligand screening.
Bottegoni, G., Rocchia, W., Rueda, M., Abagyan, R., & Cavalli, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018845 · Cited by 96
(2023) · doi:10.1021/acs.jcim.2c01270 · Cited by 95
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-citationEnhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15
Liu, J., Guo, Z., Wu, T., Roy, R. S., Quadir, F., Chen, C., & Cheng, J. (2023) · Communications Biology · doi:10.1038/s42003-023-05525-3 · Cited by 86
Fragment Informatics and Computational Fragment‐Based Drug Design: An Overview and Update
Sheng, C., & Zhang, W. (2013) · Medicinal Research Reviews · doi:10.1002/med.21255 · Cited by 80
New 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
Im, E., Jiang, Y., Stavrides, P. H., Darji, S., Erdjument-Bromage, H., Neubert, T. A., Choi, J. Y., Wegiel, J., Lee, J.-H., & Nixon, R. A. (2023) · Science Advances · doi:10.1126/sciadv.adg1925 · 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
Real-Time Imaging of the Azole Class of Antifungal Drugs in Live Candida Cells
Benhamou, R. I., Bibi, M., Steinbuch, K. B., Engel, H., Levin, M., Roichman, Y., Berman, J., & Fridman, M. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00339 · Cited by 71
Modeling of metal interaction geometries for protein–ligand docking
Seebeck, B., Reulecke, I., Kämper, A., & Rarey, M. (2008) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21818 · Cited by 67
Deep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66
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
Wang, H.-T., Wang, Z.-L., Chen, K., Yao, M.-J., Zhang, M., Wang, R.-S., Zhang, J.-H., Ågren, H., Li, F.-D., Li, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42393-1 · Cited by 53
Grover, A., Katiyar, S. P., Jeyakanthan, J., Dubey, V. K., & Sundar, D. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s7-s20 · Cited by 52
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
Pro-phagocytic function and structural basis of GPR84 signaling
Zhang, X., Wang, Y., Supekar, S., Cao, X., Zhou, J., Dang, J., Chen, S., Jenkins, L., Marsango, S., Li, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41201-0 · Cited by 46
Ding, B., Wang, J., Li, N., & Wang, W. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300508m · Cited by 45
Simoben, C., Robaa, D., Chakrabarti, A., Schmidtkunz, K., Marek, M., Lancelot, J., Kannan, S., Melesina, J., Shaik, T., Pierce, R., et al. (2018) · Molecules · doi:10.3390/molecules23030566 · Cited by 44
Leung, E. L., Luo, L. X., Li, Y., Liu, Z., Li, L. L., Shi, D. F., Xie, Y., Huang, M., Lu, L. L., Duan, F. G., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32222 · Cited by 39
Marcyk, P. T., LeBlanc, E. V., Kuntz, D. A., Xue, A., Ortiz, F., Trilles, R., Bengtson, S., Kenney, T. M. G., Huang, D. S., Robbins, N., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01777 · Cited by 40
Diverse modes of H3K36me3-guided nucleosomal deacetylation by Rpd3S
Guan, H., Wang, P., Zhang, P., Ruan, C., Ou, Y., Peng, B., Zheng, X., Lei, J., Li, B., Yan, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06349-1 · Cited by 40
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
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
Decoding the Foodome: Molecular Networks Connecting Diet and Health
Menichetti, G., Barabási, A.-L., & Loscalzo, J. (2024) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-062322-030557 · Cited by 32
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
(2024) · doi:10.1038/s41467-024-52146-3 · Cited by 32
Challenges of docking in large, flexible and promiscuous binding sites
Kotev, M., Soliva, R., & Orozco, M. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.08.010 · 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
Natarajan, S., Govindaraju, A., Rajmohamed, M., Palanisamy, P., & Abdulkader, A. (2017) · Pharmacognosy Magazine · doi:10.4103/pm.pm_57_17 · Cited by 20
Baselious, F., Robaa, D., & Sippl, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107700 · Cited by 20
Cescon, E., Bolcato, G., Federico, S., Bissaro, M., Valentini, A., Ferlin, M. G., Spalluto, G., Sturlese, M., & Moro, S. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00028 · Cited by 19
Jamal, S., Goyal, S., Shanker, A., & Grover, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129370 · Cited by 19
Nagaraju, M., McGowan, L. C., & Hamelberg, D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300432w · Cited by 18
Sortilin facilitates VLDL-B100 secretion by insulin sensitive McArdle RH7777 cells
Sparks, R. P., Guida, W. C., Sowden, M. P., Jenkins, J. L., Starr, M. L., Fratti, R. A., Sparks, C. E., & Sparks, J. D. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2016.07.096 · Cited by 17
Substrate transport and drug interaction of human thiamine transporters SLC19A2/A3
Li, P., Zhu, Z., Wang, Y., Zhang, X., Yang, C., Zhu, Y., Zhou, Z., Chao, Y., Long, Y., Gao, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-55359-8 · Cited by 17
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
Wu, Y.-W., HuangFu, W.-C., Lin, T. E., Peng, C.-H., Tu, H.-J., Sung, T.-Y., Sung, T.-Y., Yen, S.-C., Pan, S.-L., & Hsu, K.-C. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.129074 · Cited by 15
Benchmarking reverse docking through AlphaFold2 human proteome
(2024) · doi:10.1002/pro.5167 · Cited by 14
Liu, Y., Moretti, R., Wang, Y., Dong, H., Yan, B., Bodenheimer, B., Derr, T., & Meiler, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00822 · Cited by 13
Gao, Y.-D., Hu, Y., Crespo, A., Wang, D., Armacost, K. A., Fells, J. I., Fradera, X., Wang, H., Wang, H., Sherborne, B., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0072-z · Cited by 9
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Metadata
- Authors
- Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-02-27
- Volume
- 47
- Issue
- 7
- Pages
- 1750-1759
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2009423060
- Cited by
- 5,177
- References
- 4
- FWCI
- 44.22
- 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 ›
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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.