The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
Abstract
Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP > 4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“which typically results in low aqueous solubility and high metabolic clearance (Lipinski et al., 2001).”
From A Fungal-Selective Cytochrome bc1 Inhibitor Impairs Virulence and Prevents the Evolution of Drug Resistance (2016) · cites this paper as Lipinski et al., 2001
In the citing paper
“Considering the restrictive Lipinski’s rules for BBB penetration (MW ≤ 450, clogP ≤5, hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, polar surface area ≤ 90 Å2) and the calculated logBB values (Table S2), both L1 and L2 satisfy these requirements, suggesting that these compounds should be capable of crossing the BBB.”
From Bifunctional Compounds for Controlling Metal-Mediated Aggregation of the Aβ42Peptide (2012) · cites this paper as [75]
In the citing paper
“In general, PROTAC physicochemical property space falls beyond the ‘rule of 5’ (refs59,60) owing to the bifunctional nature of the molecules.”
From PROTAC targeted protein degraders: the past is prologue (2022) · cites this paper as [59]
Citations
Cites
Solvents and Solvent Effects in Organic Chemistry
Reichardt, C. L. (2002) · Wiley eBooks · doi:10.1002/3527601791 · Cited by 5,795
Abraham, M. H. (1993) · Chemical Society Reviews · doi:10.1039/cs9932200073 · Cited by 1,968
Are Crystal Structures Predictable?
Gavezzotti, A. (1994) · Accounts of Chemical Research · doi:10.1021/ar00046a004 · Cited by 922
The use of characteristic volumes to measure cavity terms in reversed phase liquid chromatography
Abraham, M. H., & McGowan, J. C. (1987) · Chromatographia · doi:10.1007/bf02311772 · Cited by 861
Abraham, M. H., Chadha, H. S., Whiting, G. S., & Mitchell, R. C. (1994) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.2600830806 · Cited by 766
Calculating log Poct from structures
Leo, A. J. (1993) · Chemical Reviews · doi:10.1021/cr00020a001 · Cited by 705
Solubility and partitioning I: Solubility of nonelectrolytes in water
Yalkowsky, S. H., & Valvani, S. C. (1980) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.2600690814 · Cited by 581
Functional group contributions to drug-receptor interactions
Andrews, P. R., Craik, D. J., & Martin, J. L. (1984) · Journal of Medicinal Chemistry · doi:10.1021/jm00378a021 · Cited by 538
Simple Method of Calculating Octanol/Water Partition Coefficient.
MORIGUCHI, I., Hirono, S., Qian, L. I. U., Nakagome, I., & MATSUSHITA, Y. (1992) · Chemical and Pharmaceutical Bulletin · doi:10.1248/cpb.40.127 · Cited by 536
Abraham, M. H. (1993) · Journal of Physical Organic Chemistry · doi:10.1002/poc.610061204 · Cited by 354
Research works printed but not linked: 3
- Lipinski, C.A. (1995) Computational alerts for potential absorption problems: profiles of clinically tested drugs. In: Tools for Oral Absorption. Part Two. Predicting Human Absorption. BIOTEC, PDD symposium, AAPS, Miami.
- Workshop on Oral Drug Delivery: Interface Between Discovery and Development. Report and Recommendations. National Institutes of Health, National Institute of General Medical Sciences. Herndon, Virginia, December 5–7, 1993.
- Lipinski, C.A., Davis, A.L., Aldridge, P.K. and Brown, C.W. (1994) Biologically relevant UV-based solubility assays. Pharmaceutical Analysis Session, 45th Pittsburgh Conf., Chicago, Oral Presentation 959.
Cited by
Sorted by citations
Items 101–200 of 1,083
Integration of virtual and high-throughput screening
Bajorath, J. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd941 · Cited by 935
Ritchie, T. J., & Macdonald, S. J. F. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.07.014 · Cited by 930
BDDCS, the Rule of 5 and drugability
Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.05.007 · Cited by 959
Schneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910
Dong, J. N., Wang, N., Yao, Z., Zhang, L., Cheng, Y., Ouyang, D., Lü, A., & Cao, D. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0283-x · Cited by 919
The influence of lipophilicity in drug discovery and design
Arnott, J. A., & Planey, S. L. (2012) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2012.714363 · Cited by 928
Computation of Octanol−Water Partition Coefficients by Guiding an Additive Model with Knowledge
Cheng, T., Zhao, Y., Li, X., Fu, L., Xu, Y., Zhang, X., Li, Y., Wang, R., & Lai, L. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700257y · Cited by 897
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
Lipophilicity in drug discovery
Waring, M. J. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441003605098 · Cited by 916
Lu, H., Zhou, Q., He, J., Jiang, Z., Peng, C., Tong, R., & Shi, J. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00315-3 · Cited by 891
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 911
Role of antioxidants in prophylaxis and therapy: A pharmaceutical perspective
Ratnam, D. V., Ankola, D. D., Bhardwaj, V., Sahana, D. K., & Kumar, M. N. V. R. (2006) · Journal of Controlled Release · doi:10.1016/j.jconrel.2006.04.015 · Cited by 885
Antimalarial drug discovery: efficacy models for compound screening
Fidock, D. A., Rosenthal, P. J., Croft, S. L., Brun, R., & Nwaka, S. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1416 · Cited by 863
The maximal affinity of ligands
Kuntz, I. D., Chen, K., Sharp, K. A., & Kollman, P. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.9997 · Cited by 859
Johnson, J. I., Decker, S., Zaharevitz, D. W., Rubinstein, L., Venditti, J. M., Schepartz, S. A., Kalyandrug, S., Christian, M. C., Arbuck, S. G., Hollingshead, M. G., et al. (2001) · British Journal of Cancer · doi:10.1054/bjoc.2001.1796 · Cited by 856
Current Challenges and Opportunities in Treating Glioblastoma
Shergalis, A. G., Bankhead, A., Luesakul, U., Muangsin, N., & Neamati, N. (2018) · Pharmacological Reviews · doi:10.1124/pr.117.014944 · Cited by 871
A Novel Chemometric Method for the Prediction of Human Oral Bioavailability
Xu, X. J., Zhang, W., Huang, C., Li, Y., Yu, H., Wang, Y., Duan, J., & Yang, L. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13066964 · Cited by 842
ADMET modeling approaches in drug discovery
Ferreira, L. L. G., & Andricopulo, A. D. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.03.015 · Cited by 857
Targeting IAP proteins for therapeutic intervention in cancer
Fulda, S., & Vucic, D. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3627 · Cited by 837
Kurkov, S. V., & Loftsson, Þ. (2012) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2012.06.055 · Cited by 860
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
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832
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
Cyclodextrins: structure, physicochemical properties and pharmaceutical applications
Jansook, P., Ogawa, N., & Loftsson, Þ. (2017) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2017.11.018 · Cited by 824
From carbohydrate leads to glycomimetic drugs
Ernst, B., & Magnani, J. L. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2852 · Cited by 835
Kelder, J., Grootenhuis, P. D. J., Bayada, D. M., Delbressine, L. P. C., & Ploemen, J.-P. H. T. M. (1999) · Pharmaceutical Research · doi:10.1023/a:1015040217741 · Cited by 819
PROteolysis TArgeting Chimeras (PROTACs) — Past, present and future
Pettersson, M., & Crews, C. M. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2019.01.002 · Cited by 830
IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics
Mohanraj, K., Karthikeyan, B. S., Vivek-Ananth, R. P., Chand, R., Aparna, S. R., Mangalapandi, P., & Samal, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-22631-z · Cited by 837
Global mapping of pharmacological space
Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006) · Nature Biotechnology · doi:10.1038/nbt1228 · Cited by 822
The Science of Antibiotic Discovery
Lewis, K. (2020) · Cell · doi:10.1016/j.cell.2020.02.056 · Cited by 848
Approaches to transport therapeutic drugs across the blood–brain barrier to treat brain diseases
Gabathuler, R. (2009) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.07.028 · Cited by 829
Antimicrobial peptides: key components of the innate immune system
Pasupuleti, M., Schmidtchen, A., & Malmsten, M. (2011) · Critical Reviews in Biotechnology · doi:10.3109/07388551.2011.594423 · Cited by 804
The rise of fragment-based drug discovery
Murray, C. W., & Rees, D. C. (2009) · Nature Chemistry · doi:10.1038/nchem.217 · Cited by 803
Rees, D. C., Congreve, M., Murray, C. W., & Carr, R. A. E. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1467 · Cited by 812
Cyclodextrins in drug delivery
Loftsson, Þ., Jarho, P., Másson, M., & Järvinen, T. (2005) · Expert Opinion on Drug Delivery · doi:10.1517/17425247.2.1.335 · Cited by 819
Medical need, scientific opportunity and the drive for antimalarial drugs
Ridley, R. G. (2002) · Nature · doi:10.1038/415686a · Cited by 792
Advances in polymeric micelles for drug delivery and tumor targeting
Kedar, U., Phutane, P., Shidhaye, S., & Kadam, V. (2010) · Nanomedicine: Nanotechnology, Biology and Medicine · doi:10.1016/j.nano.2010.05.005 · Cited by 791
Principles of encapsulating hydrophobic drugs in PLA/PLGA microparticles
Wischke, C., & Schwendeman, S. P. (2008) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2008.04.042 · Cited by 821
Buglioni, L., Raymenants, F., Slattery, A., Zondag, S. D. A., & Noël, T. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00332 · Cited by 806
Molecular Recognition and Docking Algorithms
Brooijmans, N., & Kuntz, I. D. (2003) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.32.110601.142532 · Cited by 812
Is There a Difference between Leads and Drugs? A Historical Perspective
Oprea, T. I., Davis, A. M., Teague, S. J., & Leeson, P. D. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010366a · Cited by 776
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 784
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762
Next generation antibody drugs: pursuit of the 'high-hanging fruit'
Carter, P. J., & Lazar, G. A. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.227 · Cited by 780
Targeted pharmaceutical nanocarriers for cancer therapy and imaging
Torchilin, V. P. (2007) · The AAPS Journal · doi:10.1208/aapsj0902015 · Cited by 771
Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J. A., & Wyatt, P. G. (2007) · ChemMedChem · doi:10.1002/cmdc.200700139 · Cited by 772
Intracellular Delivery by Membrane Disruption: Mechanisms, Strategies, and Concepts
Stewart, M. P., Langer, R., & Jensen, K. F. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00678 · Cited by 767
ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness
Guan, L., Yang, H., Cai, Y., Sun, L., Di, P., Li, W., Liu, G., & Tang, Y. (2018) · MedChemComm · doi:10.1039/c8md00472b · Cited by 765
The Blood-Brain Barrier in Brain Homeostasis and Neurological Diseases
Weiss, N., Miller, F., Cazaubon, S., & Couraud, P.-O. (2009) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2008.10.022 · Cited by 731
From magic bullets to designed multiple ligands
Morphy, R., Kay, C., & Ranković, Z. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03163-0 · Cited by 738
Inhibition of α-helix-mediated protein–protein interactions using designed molecules
Azzarito, V., Long, K., Murphy, N. S., & Wilson, A. J. (2013) · Nature Chemistry · doi:10.1038/nchem.1568 · Cited by 727
From nature to industry: Harnessing enzymes for biocatalysis
Buller, R., Lutz, S., Kazlauskas, R. J., Šnajdrová, R., Moore, J. C., & Bornscheuer, U. T. (2023) · Science · doi:10.1126/science.adh8615 · Cited by 759
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
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
Ekins, S., Mestres, J., & Testa, B. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707305 · Cited by 721
Pelay‐Gimeno, M., Glas, A., Koch, O., & Grossmann, T. N. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201412070 · Cited by 740
Rate-Limited Steps of Human Oral Absorption and QSAR Studies
Zhao, Y. H., Abraham, M. H., Le, J., Hersey, A., Luscombe, C. N., Beck, G., Sherborne, B. S., & Cooper, I. (2002) · Pharmaceutical Research · doi:10.1023/a:1020444330011 · Cited by 723
Handbook of Solubility Data for Pharmaceuticals
Jouyban, A. (2009) · CRC Press eBooks · doi:10.1201/9781439804889 · Cited by 722
Characteristics of Compounds that Cross the Blood-Brain Barrier
Banks, W. A. (2009) · BMC Neurology · doi:10.1186/1471-2377-9-s1-s3 · Cited by 710
Metallodrugs are unique: opportunities and challenges of discovery and development
Anthony, E. J., Bolitho, E. M., Bridgewater, H. E., Carter, O. W. L., Donnelly, J. M., Imberti, C., Lant, E. C., Lermyte, F., Needham, R. J., Palau, M., et al. (2020) · Chemical Science · doi:10.1039/d0sc04082g · Cited by 727
Subcellular targeting strategies for drug design and delivery
Rajendran, L., Knölker, H., & Simons, K. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2897 · Cited by 714
Burdette, B. E., Esparza, A. N., Zhu, H., & Wang, S. (2021) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.02.006 · Cited by 721
Baird, J. A., Eerdenbrugh, B. V., & Taylor, L. S. (2010) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22197 · Cited by 704
The Lipophilic Bullet Hits the Targets: Medicinal Chemistry of Adamantane Derivatives
Wanka, L., Iqbal, K., & Schreiner, P. R. (2013) · Chemical Reviews · doi:10.1021/cr100264t · Cited by 712
Virtual screening with AutoDock: theory and practice
Cosconati, S., Forli, S., Perryman, A. L., Harris, R. M., Goodsell, D. S., & Olson, A. J. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.484460 · Cited by 716
Structure-based maximal affinity model predicts small-molecule druggability
Cheng, A. C., Coleman, R. G., Smyth, K. T., Cao, Q., Soulard, P., Caffrey, D. R., Salzberg, A. C., & Huang, E. S. (2007) · Nature Biotechnology · doi:10.1038/nbt1273 · Cited by 714
Physicochemical Properties of Antibacterial Compounds: Implications for Drug Discovery
O’Shea, R., & Moser, H. E. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm700967e · Cited by 689
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 703
Virtual ligand screening: strategies, perspectives and limitations
Klebe, G. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.05.012 · Cited by 679
Antibacterial Natural Products in Medicinal Chemistry—Exodus or Revival?
von Nussbaum, F., Brands, M., Hinzen, B., Weigand, S., & Häbich, D. (2006) · Angewandte Chemie International Edition · doi:10.1002/anie.200600350 · Cited by 682
Direct C(sp 3 )–H Cross Coupling Enabled by Catalytic Generation of Chlorine Radicals
Shields, B. J., & Doyle, A. G. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b08397 · Cited by 668
Biocompatible surfactants for water-in-fluorocarbon emulsions
Holtze, C., Rowat, A. C., Agresti, J. J., Hutchison, J. B., Angile, F. E., Schmitz, C., Köster, S., Duan, H., Humphry, K. J., Scanga, R. A., et al. (2008) · Lab on a Chip · doi:10.1039/b806706f · Cited by 667
Zhou, Y., Zhang, Y., Lian, X., Li, F., Wang, C., Zhu, F., Qiu, Y., & Chen, Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab953 · Cited by 676
Mannhold, R., Poda, G. I., Ostermann, C., & Tetko, I. V. (2008) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21494 · Cited by 663
Features of Selective Kinase Inhibitors
Knight, Z. A., & Shokat, K. M. (2005) · Chemistry & Biology · doi:10.1016/j.chembiol.2005.04.011 · Cited by 676
Physiologically-Based Pharmacokinetics in Drug Development and Regulatory Science
Rowland, M., Peck, C., & Tucker, G. (2011) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100540 · Cited by 662
The blood-brain barrier: an engineering perspective
Wong, A. D., Ye, M., Levy, A. F., Rothstein, J. D., Bergles, D. E., & Searson, P. C. (2013) · Frontiers in Neuroengineering · doi:10.3389/fneng.2013.00007 · Cited by 662
Structure-based virtual screening: an overview
Lyne, P. D. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02483-2 · Cited by 671
The past, present, and future of antibiotics
Cook, M. A., & Wright, G. D. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abo7793 · Cited by 690
Protein tyrosine phosphatase 1B inhibitors for diabetes
Johnson, T., Ermolieff, J., & Jirousek, M. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd895 · Cited by 660
Protein degraders enter the clinic — a new approach to cancer therapy
Chirnomas, D., Hornberger, K. R., & Crews, C. M. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00736-3 · Cited by 658
Current trends and future perspectives of solid dispersions containing poorly water-soluble drugs
Vo, C. L.-N., Park, C., & Lee, B. (2013) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2013.09.007 · Cited by 656
From Sewer to Saviour—Targeting the Lymphatic System to Promote Drug Exposure and Activity
Trevaskis, N. L., Kaminskas, L. M., & Porter, C. J. H. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4608 · Cited by 654
Lukyanov, A. N., & Torchilin, V. P. (2004) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2003.12.004 · Cited by 654
The impact of natural products upon modern drug discovery
Ganesan, A. (2008) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2008.03.016 · Cited by 661
Efficient Drug Lead Discovery and Optimization
Jorgensen, W. L. (2009) · Accounts of Chemical Research · doi:10.1021/ar800236t · Cited by 661
Stimuli-Responsive Therapeutic Metallodrugs
Wang, X., Wang, X., Jin, S., Nafees, M., & Guo, Z. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00209 · Cited by 654
Use of molecular docking computational tools in drug discovery
Stanzione, F., Giangreco, I., & Cole, J. C. (2021) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2021.01.004 · Cited by 689
Computational methods in drug discovery
Leelananda, S. P., & Lindert, S. (2016) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.12.267 · Cited by 675
Luo, T.-T., Lu, Y. W., Yan, S., Xiao, X., Rong, X., & Guo, J. (2019) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-019-3064-0 · Cited by 645
Erlanson, D. A., McDowell, R. S., & O'Brien, T. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm040031v · Cited by 645
Grey Goo on the Skin? Nanotechnology, Cosmetic and Sunscreen Safety
Nohynek, G. J., Lademann, J., Ribaud, C., & Roberts, M. S. (2007) · Critical Reviews in Toxicology · doi:10.1080/10408440601177780 · Cited by 644
Ishikawa, M., & Hashimoto, Y. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm101356p · Cited by 652
Graph Convolutional Policy Network for Goal-Directed Molecular Graph GenerationPreprint
You, J., Liu, B., Ying, R., Pande, V. S., & Leskovec, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02473 · Cited by 688
Four-Membered Ring-Containing Spirocycles: Synthetic Strategies and Opportunities
Carreira, E. M., & Fessard, T. C. (2014) · Chemical Reviews · doi:10.1021/cr500127b · Cited by 643
The influence of lead discovery strategies on the properties of drug candidates
Keserű, G. M., & Makara, G. M. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2796 · Cited by 647
BDDCS Applied to Over 900 Drugs
Benet, L. Z., Broccatelli, F., & Oprea, T. I. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9290-9 · Cited by 651
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656
Clark, D. E. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js9804011 · Cited by 635
Biophysical properties of the clinical-stage antibody landscape
Jain, T., Sun, T., Durand, S., Hall, A., Houston, N. R., Nett, J. H., Sharkey, B., Bobrowicz, B., Caffry, I., Yu, Y., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616408114 · Cited by 629
Metadata
- Authors
- Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J.
- Affiliations
- Pfizer (United States) (US)
- Corresponding author
- Lipinski, Christopher A
- Venue
- Advanced Drug Delivery Reviews
- Publisher
- Elsevier BV
- Published
- 2001-03
- Volume
- 46
- Issue
- 1-3
- Pages
- 3-26
- ISSN
- 0169-409X (ISSN-L)
- OpenAlex
- W2135732933
- Cited by
- 18,139
- References
- 51
- Citations by year
- 2026: 869 · 2025: 962 · 2024: 1264 · 2023: 1296 · 2022: 1248 · 2021: 1459 · 2020: 1317 · 2019: 945 · 2018: 876 · 2017: 791 · 2016: 790 · 2015: 724 · 2014: 752 · 2013: 741 · 2012: 708
- FWCI
- 19.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Drug Delivery Reviews (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Crystallization and Solubility Studies
- Keywords
- drug solubility, intestinal permeability, Rule of 5, molecular weight, lipophilicity, polymorphism, linear free energy relationships, thermodynamic solubility, solubility prediction, drug absorption
- MeSH terms
- Adsorption, Chemistry, Physical, Computers, Pharmaceutical Preparations, Permeability, Pharmacology, Solubility, Thermodynamics, Chemical Phenomena
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
