The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties
Abstract
Molecular polar surface area (PSA), i.e., surface belonging to polar atoms, is a descriptor that was shown to correlate well with passive molecular transport through membranes and, therefore, allows prediction of transport properties of drugs. The calculation of PSA, however, is rather time-consuming because of the necessity to generate a reasonable 3D molecular geometry and the calculation of the surface itself. A new approach for the calculation of the PSA is presented here, based on the summation of tabulated surface contributions of polar fragments. The method, termed topological PSA (TPSA), provides results which are practically identical with the 3D PSA (the correlation coefficient between 3D PSA and fragment-based TPSA for 34 810 molecules from the World Drug Index is 0.99), while the computation speed is 2-3 orders of magnitude faster. The new methodology may, therefore, be used for fast bioavailability screening of virtual libraries having millions of molecules. This article describes the new methodology and shows the results of validation studies based on sets of published absorption data, including intestinal absorption, Caco-2 monolayer penetration, and blood-brain barrier penetration.
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Cites
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058
The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates
Roughley, S. D., & Jordan, A. M. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200187y · Cited by 2,725
Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939
Self-citationMedicinal chemical properties of successful central nervous system drugs
Pajouhesh, H., & Lenz, G. R. (2005) · NeuroRX · doi:10.1602/neurorx.2.4.541 · Cited by 1,535
Physiochemical drug properties associated with in vivo toxicological outcomes
Hughes, J. D., Blagg, J., Price, D. A., Bailey, S., DeCrescenzo, G. A., Devraj, R. V., Ellsworth, E., Fobian, Y. M., Gibbs, M. E., Gilles, R. W., et al. (2008) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2008.07.071 · Cited by 946
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
Getting Across the Cell Membrane: An Overview for Small Molecules, Peptides, and Proteins
Yang, N. J., & Hinner, M. J. (2015) · Methods in Molecular Biology · doi:10.1007/978-1-4939-2272-7_3 · Cited by 826
Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates
Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.08.013 · Cited by 835
Halogen atoms in the modern medicinal chemistry: hints for the drug design
Hernandes, M., Cavalcanti, S. M., Moreira, D. R., de Azevedo Junior, W., & Leite, A. C. (2010) · Current Drug Targets · doi:10.2174/138945010790711996 · Cited by 723
Diversity-oriented synthesis: Exploring the intersections between chemistry and biology
Tan, D. S. (2005) · Nature Chemical Biology · doi:10.1038/nchembio0705-74 · Cited by 605
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
Kim, C., Chandrasekaran, A., Huan, T. D., Das, D., & Ramprasad, R. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b02913 · Cited by 513
Orally Absorbed Cyclic Peptides
Nielsen, D. S., Shepherd, N. E., Xu, W., Lucke, A. J., Stoermer, M. J., & Fairlie, D. P. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00838 · Cited by 445
Drug research: myths, hype and reality
Kubinyi, H. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1156 · Cited by 348
Lenselink, E. B., ten Dijke, N., Bongers, B., Papadatos, G., van Vlijmen, H. W. T., Kowalczyk, W., IJzerman, A. P., & van Westen, G. J. P. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0232-0 · Cited by 334
Virshup, A. M., Contreras-García, J., Wipf, P., Yang, W., & Beratan, D. N. (2013) · Journal of the American Chemical Society · doi:10.1021/ja401184g · Cited by 294
Xiong, B., Wang, Y., Chen, Y., Xing, S., Liao, Q., Chen, Y., Li, Q., Li, W., & Sun, H. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00910 · Cited by 232
How machine learning can help select capping layers to suppress perovskite degradation
Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J. J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17945-4 · Cited by 189
Nutrition, Well-Being and Health
(2012) · doi:10.5772/1864 · Cited by 178
Rodríguez-Rodríguez, C., Telpoukhovskaia, M., & Orvig, C. (2012) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2012.03.008 · Cited by 151
Ackloo, S., Al-awar, R., Amaro, R. E., Arrowsmith, C. H., Azevedo, H., Batey, R. A., Bengio, Y., Betz, U. A. K., Bologa, C. G., Chodera, J. D., et al. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00363-z · Cited by 112
Issahaku, A. R., Mukelabai, N., Agoni, C., Rudrapal, M., Aldosari, S. M., Almalki, S. G., & Khan, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-22668-1 · Cited by 93
Chuang, S.-Y., Lin, Y.-K., Lin, C.-F., Wang, P.-W., Chen, E.-L., & Fang, J.-Y. (2017) · Nutrients · doi:10.3390/nu9121304 · Cited by 85
Drug‐Like Properties in Macrocycles above MW 1000: Backbone Rigidity versus Side‐Chain Lipophilicity
Furukawa, A., Schwochert, J., Pye, C. R., Asano, D., Edmondson, Q. D., Turmon, A. C., Klein, V. G., Ono, S., Okada, O., & Lokey, R. S. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202004550 · Cited by 79
Monomeric Targeted Protein Degraders
Hanan, E. J., Liang, J., Wang, X., Blake, R. A., Blaquiere, N., & Staben, S. T. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00093 · Cited by 69
GFscore: a general nonlinear consensus scoring function for high-throughput docking.
Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., & Morelli, X. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600758 · Cited by 51
Fragment-Based Screening Maps Inhibitor Interactions in the ATP-Binding Site of Checkpoint Kinase 2
Silva-Santisteban, M. C., Westwood, I. M., Boxall, K., Brown, N., Peacock, S., McAndrew, C., Barrie, E., Richards, M., Mirza, A., Oliver, A. W., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065689 · Cited by 31
“In addition, typical Rule-of-Three-based physicochemical property filters [48] were used such as, ClogP#3 [69], hydrogen bond acceptors #5 and hydrogen bond donors #3, a topological polar surface area (TPSA) #75 Å(2) [70], and the number of rotatable bonds #3.”
MethodAvram, S., Curpan, R., Halip, L., Bora, A., & Oprea, T. I. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00826 · Cited by 18
1,5-Benzodiazepin-2(3H)-ones: In Vitro Evaluation as Antiparkinsonian Agents
Ortíz de Zárate, A., Pérez-Torralba, M., Bonet Isidro, I., López, C., Claramunt, R. M., Martínez-Casanova, D., Sánchez-Vera, I., Jiménez-González, J., & Lavandera, J. L. (2021) · Antioxidants · doi:10.3390/antiox10101584 · Cited by 11
“Regarding the potential for blood–brain barrier (BBB) penetration capacity, the Total Polar Surface Area (TPSA) has been used as a predictor [108], as drugs aimed at the CNS tend to have lower polar surface areas.”
MethodZhang, J., Shi, Q., Yates, R., Liu, Z., & Gan, J. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c07872 · Cited by 8
Deep learning molecular interaction motifs from receptor structures alone
Kim, S., Oh, S., Woo, H., Sim, J., Seok, C., & Park, H. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01055-8 · Cited by 3
Metadata
- Authors
- Ertl, P., Rohde, B., & Selzer, P.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2000-09-19
- Volume
- 43
- Issue
- 20
- Pages
- 3714-3717
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2033495141
- Cited by
- 3,084
- References
- 1
- FWCI
- 28.80
- Type
- journal article
- Language
- en
- Open access
- closed
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