Cited by 869Computational Drug Discovery MethodsDrug Solubulity and Delivery SystemsChemical Synthesis and Analysis

The DOI matches its Crossref record; 289 of the 295 references it cites were found in a registry or an index.

Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates

Open access

Doak, Bradley Croy, Over, Bjӧrn, Giordanetto, Fabrizio, Kihlberg, Jan·2014·Chemistry & Biology

DOI 10.1016/j.chembiol.2014.08.013

Open this paper in CoChat — free account Open PDF

Abstract

The rule of 5 (Ro5) is a set of in silico guidelines applied to drug discovery to prioritize compounds with an increased likelihood of high oral absorption. It has been influential in reducing attrition due to poor pharmacokinetics over the last 15 years. However, strict reliance on the Ro5 may have resulted in lost opportunities, particularly for difficult targets. To identify opportunities for oral drug discovery beyond the Ro5 (bRo5), we have comprehensively analyzed drugs and clinical candidates with molecular weight (MW) > 500 Da. We conclude that oral drugs are found far bRo5 and properties such as intramolecular hydrogen bonding, macrocyclization, dosage, and formulations can be used to improve bRo5 bioavailability. Natural products and structure-based design, often from peptidic leads, are key sources for oral bRo5 drugs. These insights should help guide the design of oral drugs in bRo5 space, which is of particular interest for difficult targets.

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

Crossref · 10.1016/j.chembiol.2014.08.013

Cites

289 of 289 references verified0 citation errors · 0 not checkable

Statistics

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.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
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
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484
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
Lead- and drug-like compounds: the rule-of-five revolution
Lipinski, C. A. (2004) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2004.11.007 · Cited by 6,455
Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J. A., & Humblet, C. C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,614
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
Drug-like properties and the causes of poor solubility and poor permeability
Lipinski, C. A. (2000) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/s1056-8719(00)00107-6 · Cited by 3,776
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
Membrane transporters in drug development
The International Transporter Consortium, Giacomini, K. M., Huang, S.-M., Tweedie, D. J., Benet, L. Z., Brouwer, K. L. R., Chu, X., Dahlin, A., Evers, R., Fischer, V., et al. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3028 · Cited by 3,340
Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties
Ertl, P., Rohde, B., & Selzer, P. M. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000942e · Cited by 3,149

Flagged first, then by citations

Show all 295 references

6 of the 295 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Bilello, J.P., Dousson, C.B., Colla, M.L., Chapron, C., Bhadresa, S., Camire, M., Serra, I., Gillum, J.M., Lallos, L.B., McCarville, J.F., et al. (2011). Antiviral and Preclinical Profiles of HCV NS5A Inhibitors IDX380 and IDX719. Proceedings of the 18th International Symposium on Hepatitis C Virus and Related Viruses, Seattle; September 8–12, 2011.

Sorted by citations

Items 701–800 of 869

Optimisation of Passive Permeability for Oral Absorption
Pike, A., & Storer, R. I. (2021) · Royal Society of Chemistry eBooks · doi:10.1039/9781788016414-00036 · Cited by 1
Theoretical Analysis of Anticancer Cellular Effects of Glycoside Amides
Tsanov, V., & Tsanov, H. (2021) · Anti-Cancer Agents in Medicinal Chemistry · doi:10.2174/1871520621666210903122831
Studie av en medeltida husgrund : en analys av bronsfynd från husgrund 6, Västergarn, Gotland
Olsson, T. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
In Silico ADME: Rule-Based Systems
Talevi, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-51519-5_148-2
Repurposing Multi-Targeting Plant Natural Product Scaffolds In Silico Against SARS-CoV-2 Non-Structural Proteins Implicated in Viral PathogenesisPreprint
de Leon, V. N., Manzano, J. A., Pilapil, D. Y. H., Fernandez, R. A. T., Ching, J. K., Quimque, M. T. J., Notarte, K. I., & Macabeo, A. P. G. (2021) · ChemRxiv · doi:10.26434/chemrxiv.14125433
Drug Transport and Membrane Transport Proteins
Welch, M. A., & Swaan, P. W. (2021) · Wiley eBooks · doi:10.1002/0471266949.bmc027.pub3
Targeting and Repurposing
Balas, E. A. (2018) · Wiley eBooks · doi:10.1002/9781119225898.ch14
Screening for inhibitors of main protease in SARS-CoV-2: in silico and in vitro approach avoiding secondary amidesPreprint
Yamamoto, K. Z., Yasuo, N., & Sekijima, M. (2021) · doi:10.33774/chemrxiv-2021-41r69
Allosteric Modulation
Doller, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00057-8
Faculty Opinions recommendation of Target-Based Evaluation of "Drug-Like" Properties and Ligand Efficiencies.
Lowe, J. A., III (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.740105200.793585637
Discovery of a Highly Potent and Selective Degrader Targeting Hematopoietic Prostaglandin D Synthase via in Silico DesignPreprint
Yokoo, H., Shibata, N., Endo, A., Ito, T., Yanase, Y., Murakami, Y., Fujii, K., Saeki, Y., Naito, M., Aritake, K., et al. (2021) · ChemRxiv · doi:10.26434/chemrxiv.14739486
Systematic Exploration of Passive Permeability in Tetrapeptides with Hydrogen‐Bond‐Accepting Amino Acid Side Chains
Shimizu, H., & Renslo, A. R. (2022) · ChemMedChem · doi:10.1002/cmdc.202200204
In Silico ADME: Rule-Based Systems
Talevi, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-84860-6_148
In silico Characterization, ADMET prediction and Molecular Docking studies of few 2/3 carboxylate-4/5/6-monosubstitued indole derivatives as potential GSK-3β inhibitors
Ramesh, D. K., & Chatpalliwar, V. A. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns3.9119
Design and Synthesis of Thiazole-Based Small Molecules to Inhibit the Inflammatory Cytokine Oncostatin M
Randall, S. (2022) · doi:10.18122/td.1950.boisestate
Medicinal plant derived drugs and their treatment for human diseases like cancer: A review
Chanu, M. T., & Singh, A. S. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.16.2.1128 · Cited by 1
Taming the Beast: How to Optimize DMPK–PD Properties of Oral Degraders
Rynn, C., Vyver, A. J. V. D., Walz, A., Cantrill, C., & Wittwer, M. (2022) · Wiley eBooks · doi:10.1002/9783527836208.ch12
PROTAC ® Protein Degraders: Bridging the Divide from Chemical Biology Tools to Clinical Candidates
Araujo, E. M. V., & Hornberger, K. R. (2022) · Wiley eBooks · doi:10.1002/9783527836208.ch13
AN IN-SILICO STUDY OF SOME NATURAL AND SYNTHETIC COMPOUNDS AS POTENTIAL INHIBITORS FOR FACTOR XA
صباغ, غ., Al-Beik, L., & Hadid, I. (2022) · Bulletin of Pharmaceutical Sciences Assiut · doi:10.21608/bfsa.2022.271593
Drug Properties
Khojasteh, S. C., Wong, H., Zhang, D., & Hop, C. E. C. A. (2022) · Springer eBooks · doi:10.1007/978-3-031-10691-0_2
In silico Screening, Docking, and Redesigning of Traditional Chinese Medicinal Compounds Against Streptococcus pneumoniae Glycosyl Hydrolase GHIP and Peptidoglycan Hydrolase LytB
Busania, V. N., Canilao, D. M. C., Endriga, M. A., & Frio, E. J. L. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995433
“Previous studies have indicated that the oral druggable space extends beyond Ro5 [31].”
BackgroundInfluential
Studi In Silico Senyawa Daun Sirsak (Annona muricata L.) Sebagai Inhibitor BRAF V600E Pada Kanker Melanoma
Dewayani, A. R., Ghaliya, S., Parameswari, N., Pribadi, A. P. A., Ahadi, H. M., Aulifa, D. L., Elaine, A. A., & Sitinjak, B. D. P. (2023) · Jurnal Farmasi Udayana · doi:10.24843/jfu.2022.v11.i02.p07
Drug discovery: Standing on the shoulders of giants
Miller, S. M., Moos, W. H., Munk, B. H., Munk, S. A., Hart, C. P., & Spellmeyer, D. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824304-6.00001-8
Identification of potential inhibitor against CTX-M-3 and CTX-M-15 proteins: an in silico and in vitro study
Kar, B., Kundu, C. N., Singh, M. K., Dehury, B., Pati, S., & Bhattacharya, D. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2192811
“It was stated in Lipinski’s Rule of Five (RO5) that drugs with more than two or three violations will not be suitable as orally active drugs, so compounds with one or no violations were selected for further study (Doak et al., 2014).”
Background
On the Possibility to Build up the QSAR Model of Different Kinds of Inhibitory Activity for a Large List of Human Intestinal Transporter Using Quasi-SMILES
Achary, P. G. R., Krishna, P. K., Toropova, A. P., & Toropov, A. A. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-28401-4_10
IN SILICO SCREENING AND IN VIVO EVALUATION OF POTENTIAL ANALGESIC EFFECT OF NEW NEUROTENSIN (NT8-13) PEPTIDE MIMETICS
Mihaylova, S. G. (2023) · FARMACIA · doi:10.31925/farmacia.2023.3.12
Friedolanostanes from Garcinia benthamii Pierre: in silico Investigation of Anti-inflammatory Activity Targeting the NF-κB p50 Subunit
Ting, J. U., Ragasa, C. Y., Lerom, R. R., Shen, C., Ng, V. A. S., Tan, M. C. S., & Oyong, G. G. (2023) · Asian Journal of Chemistry · doi:10.14233/ajchem.2023.27960
Exploring Brazilian Natural Products as Potential Bioactive Compounds against Trypanosoma cruzi by Targeting Squalene Synthase
Paschoal, C. R. S., Rabelo, V. W., de Oliveira, T. F., & Abreu, P. A. (2023) · Letters in Drug Design & Discovery · doi:10.2174/1570180820666230816141241
Machine Learning-based Prediction of FGFR Kinase Inhibitors: A comparative study and implications for Targeted Cancer Treatment
Adil, M., Karnati, G., & Sujitha, D. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307192
PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M., Cho, K., & No, K. T. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05588-3
“This remarkable attribute makes them particularly promising as potential sources of oral drugs that surpass the limitations imposed by Lipinski’s rule of five, opening up new avenues for drug development and providing improved therapeutic options for patients [14].”
Background
Targeting the Major Capsid Protein of Rock Bream Iridovirus by Trapa Natans Phytochemical Compounds; a Molecular Docking Approach
Wikumpriya, G. C., Rajapaksha, L. G. T. G., Bandara, B. P. D. M., & Prabhatha, M. W. S. (2023) · Genetics of Aquatic Organisms · doi:10.4194/ga688
“However, it was suggested that around 40% of these drugs have been failed during the trials due to the positive ranges of toxicity (Doak et al., 2014; Kimmelman & Federico, 2017).”
Background
Molecular docking and pharmacophore analysis of trisindoline 1 against human topoisomerase II and human derived growth factor
Nurhayati, A. P. D., Indiani, A. M., Oktyasti, E. N., Santoso, M., Setiawan, E., Jadid, N., & Amin, M. N. G. (2024) · AIP conference proceedings · doi:10.1063/5.0200770
Aplicación de nuevas isoxazolidinas quirales en síntesis orientada a la diversidad
Aranda, A. E. (2024) · doi:10.14201/gredos.159160
The potential of natural products for the suppression of SARS-CoV-2 replication
Sanuki, S., Tagawa, M., Saito, R., & Maegawa, H. (2024) · Studies in natural products chemistry · doi:10.1016/b978-0-443-22214-6.00015-6
Integrative Computational Design of PD-L1 Inhibitors, Utilizing MD Simulations and DFT Studies to Guide Cancer Treatment through Atomic and Electronic Properties
Varude, P., & Satish, D. (2024) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524500881
Unraveling the Potential of Marchantia Polymorpha L.-Derived Compounds as Innovative Therapeutic Candidates for Cervical Cancer Via in Silico AnalysisPreprint
Putra, A. D., Rahman, A. T., Darmawan, N. S., Syariatin, L., & Permatasari, D. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5099902
Unveiling Novel Vegfr-2 Inhibitors: Qsar Modeling, Molecular Docking, Md and Mm-Gbsa Calculations, Pharmacokinetics Profiling and Dft StudiesPreprint
Wasai, S., Patil, R. B., Uzairu, A., Ibrahim, M. T., Abdullahi, M., & Saje, B. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5154722
Bulk Measurement of Membrane Permeability for Random Cyclic Peptides in Living Cells to Guide Drug Development
Nielsen, A. L., Bartling, C. R. O., Zarda, A., Sadeleer, N. D., Neeser, R. M., Schwaller, P., Strømgaard, K., & Heinis, C. (2025) · Angewandte Chemie · doi:10.1002/ange.202500493
Cytotoxic activity of methanolic and ethanolic extract of Aquilaria agallocha Roxb. heartwood against healthy fibroblast and breast cancer cells
Batır, M. B., BATIR, S., Goral, F., Tan, S. A., Çam, F. S., & Kurt, F. Ö. (2025) · Biotech Studies · doi:10.38042/biotechstudies.1672789
Strategy for Accessing 2,5‐Diketopiperazines to Elucidate the Absolute Configuration at the α‐Position of α‐Amino Acid Derivatives via 1H‐NMR Spectroscopy
Nii, K., Ebihara, A., Morimasa, Y., & Tamiya, M. (2025) · Asian Journal of Organic Chemistry · doi:10.1002/ajoc.202500239
Spiramycin: The past and future of an antibiotic with pleiotropic effects in the therapy of community-acquired infections
Butranova, O. I., Zyryanov, S. K., & Abramova, A. A. (2024) · Pharmacy & Pharmacology · doi:10.19163/2307-9266-2024-12-2-150-171
Design of Colchicine‐Based Tubulin Inhibitors as Anticancer Agents by In Silico Analysis of Colchicine‐Based Tubulin Inhibitors: 3D‐QSAR, Molecular Docking, and Molecular Dynamics Simulations for Anticancer Drug Development
Bhandari, S. V., Anardi, S. B., Nighot, S., Gajare, S. S., Chaudhari, S. Y., Bante, G. P., Shitole, A. V., & Ghanwat, S. R. (2025) · Chemistry & Biodiversity · doi:10.1002/cbdv.202500361
Inducers of Targeted Protein Degradation as Drug Candidates for Neurodegenerative Disorders
Tomoshige, S., & Ishikawa, M. (2025) · Wiley eBooks · doi:10.1002/9783527844678.ch11
Glycosylated 18β-glycyrrhetinic acid derivatives as promising inhibitors of the SARS-CoV-2 main protease
Liao, E.-Y., Chou, S.-C., Huang, T.-Y., Huang, S.-C., Yu, T., Chou, F., & Wu, T. (2025) · RSC Advances · doi:10.1039/d5ra04664e
Targeting Ovarian Cancer With Pyrimidine Nucleobase and Nucleoside Analogs: Synthesis, In Vitro Cytotoxic Activity, and Molecular Docking
Zivali, H., Akdağ, A., Demirkan, B., Durmaz, İ., & Tunçbilek, M. (2025) · Archiv der Pharmazie · doi:10.1002/ardp.70117
“All compounds possess molecular weights below 500 Da, logP values under 5, and hydrogen ‐ bonding/donor counts that comply with Lipinski's Rule of Five, indicating favorable structural characteristics for oral bioavailability [33 – 35].”
PROTACs as New Tools to Study Platelet Function
Trory, J. S., May, C., & Hers, I. (2025) · Springer eBooks · doi:10.1007/978-3-031-96344-5_27
Mesoporous silicon for solubility enhancement of drugs
Viswalingam, V., & Kumar, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24802-3.00002-0
Structure-based drug repurposing and experimental validation of inhibitors targeting LigaseD from Mycobacterium tuberculosis
Saini, A., Sil, A., Beura, S., & Das, A. K. (2025) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2025.110645
Computational Investigation of Rifampicin as a Multitarget Inhibitor of Melanin Biosynthesis via Molecular Docking and Dynamics Simulations
Liang, X., Xu, Y., & Hyun, C. (2025) · Oriental Journal Of Chemistry · doi:10.13005/ojc/410501
Additional file 1 of Anti-HIV reverse transcriptase plant polyphenolic natural products with in silico inhibitory properties on seven non-structural proteins vital in SARS-CoV-2 pathogenesis
de Leon, V. N., Manzano, J. A., Pilapil, D. Y. H., Fernandez, R. A. T., Ching, J. K., Quimque, M. T. J., Agbay, J. C. M., Notarte, K. I. R., & Macabeo, A. P. G. (2021) · Figshare · doi:10.6084/m9.figshare.14998947
Additional file 1 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654146
Additional file 2 of DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles
Gu, Y., Wang, Y., Zhu, K., Li, W., Liu, G., & Tang, Y. (2024) · Figshare · doi:10.6084/m9.figshare.24953278
Additional file 7 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654164
Additional file 2 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654149
Additional file 1 of DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles
Gu, Y., Wang, Y., Zhu, K., Li, W., Liu, G., & Tang, Y. (2024) · Figshare · doi:10.6084/m9.figshare.24953129
Additional file 4 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654155
Additional file 6 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654161
Additional file 3 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654152
Additional file 9 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654170
Additional file 5 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654158
Additional file 1 of Predicting for anti-(mutant) SARS-CoV-2 and anti-inflammation compounds of Lianhua Qingwen Capsules in treating COVID-19
Hong, L., He, M., Li, S., & Zhao, J. (2022) · Figshare · doi:10.6084/m9.figshare.20267411
Additional file 2 of SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides
Zin, P. P. K., Williams, G. J., & Fourches, D. (2020) · Figshare · doi:10.6084/m9.figshare.12114765
Additional file 8 of PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing
Cho, M. H., Cho, K.-H., & No, K. T. (2024) · Figshare · doi:10.6084/m9.figshare.26654167
Optimizing Intracellular Transport of Antimicrobial Metallohelices Delivers Selective Nanomolar Potency in E. coli
Postings, M. L., Rogers, N. J., Shreeve, G., Song, H. L., Clarkson, G. J., Mistry, A., Moat, J., Taylor-Joyce, G., Waterfield, N. R., & Scott, P. (2026) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.5c05039
“8 Compounds that are outside the traditional rule-of-five chemical space 9,10 provide new opportunities, given their potential to address “difficult to drug” targets, e.g., large, flat, or groove-shaped binding sites such as those found, for example, in DNA or formed during protein − protein…”
An in silico studies of Solanum erianthum compounds as PPAR-α and TGF-βR1 modulators in oxidative stress-induced liver injury
Ayenero, M. E., & Oduola, T. M. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100197 · Cited by 1
Black Gold in Medicine: Rediscovering the Pharmacological Potential
Hashimova, U., Gaisina, A., & Safikhanova, K. (2026) · Molecules · doi:10.3390/molecules31030408
Macro-Equi-Diff (MED): Scaffold-based Macrocycles Generation Using Equivariant DiffusionPreprint
Kambampati, S. S., Anumandla, S., Guttula, S. L., Kavadi, V. R., Gogte, S., & Kondaparthi, V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.05.703948
Biological Effects of Novel Synthetic Guanidine Derivatives Targeting Leishmania (Viannia) braziliensis
Dias-Lopes, G., dos Anjos, L. R., da Cruz, S. M. X., Abrão, C. D., Gonçalves, M. E. P., Souza‐Silva, F., Fabišíková, A., Pérez, E. R., & Alves, C. R. (2026) · Molecules · doi:10.3390/molecules31040629
“Despite this, these two compounds showed no leishmanicidal activity against promastigotes of L. ( V. ) braziliensis , suggesting that favorable physicochemical parameters alone are not sufficient to ensure bioactivity [34].”
Exploring the identification of PKB/Akt and its interaction with docking, molecular dynamics simulations, and physiochemical properties in the echinocystic acid complex
Priyadharshini, S. D., Gayathiri, E., Pratheep, T., Selvam, K., Nagarajan, S., Pandian, R., Prakash, P., & Tamilselvan, T. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100237
Therapeutic Potential of Amyloid‐β Interactors in Rapidly Progressive Alzheimer's Disease—An In Silico Study
Saeed, S., Khan, S., Noor, A., Zerr, I., & Zafar, S. (2026) · Molecular Informatics · doi:10.1002/minf.70024
“Quinestrol and estradiol benzoate are estrogen receptor agonists approved for hormone replacement indications and can also scavenge radicals and boost mitochondrial antioxidants to counteract FH-related oxidative stress [62, 63].”
Machine Learning-guided Virtual Screening and Molecular Dynamics Simulations for Identifying Potent FGFR1 Inhibitors as Anti-neoplastic Agents
Shrestha, R., Poudel, M., Chhetri, S. P., Tamang, A., Parajuli, N., C., S., Shrestha, A., Shrestha, T., Bharati, S., Maharjan, B., et al. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-10436-x · Cited by 1
Artificial Intelligence for predicting drug-likeness and bioavailability
Rajak, B. K., & Rani, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44415-9.00011-9
Développement d'une plateforme de prédiction in silico des propriétés ADME-Tox
Canault, B. (2018) · theses.fr (ABES) · doi:10.70675/562e0758zcff7z49c4za806z3dded388daa0
New hydroxynaphthalene and quinoline derivatives effective against Trypanosoma and Leishmania. Synthesis, bioassays and molecular modeling studyPreprint
Goněc, T., Strhársky, T., Vettorazzi, M., Gutiérrez, L. J., Čudová, M., Kauerova, T., Šimurdová, M., Oravec, M., Kos, J., Kollár, P., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6516259
Conception, synthèse, pharmacomodulation et évaluation biologique de petites molécules inhibitrices du TNFα
Mascret, A. (2020) · theses.fr (ABES) · doi:10.70675/233ae09bzdb65z4aefzb47dzfe949ddce13e
Développement d’inhibiteurs d’interaction protéine-protéine sélectifs des bromodomaines
Carrasco, K. (2022) · theses.fr (ABES) · doi:10.70675/c6287e64z4b25z4bf4z848bz0244c2289110
Disrupting E3 ubiquitin ligase protein–protein interactions in cancer: chemical modalities, mechanisms, and therapeutic opportunities
Kamel, E. M., Khadrawy, S. M., Ali, M. A. M., Ahmed, N. A., Yassin, N. Y. S., Alkhedhairi, S., Alkhayl, F. F. A., & Lamsabhi, A. M. (2026) · Biochemical Pharmacology · doi:10.1016/j.bcp.2026.117951
Développement et applications de réactions d'hétérocycloaddition pour la diversification structurale et le marquage de molécules à visée thérapeutique
Maujean, T. (2022) · doi:10.70675/55371194zc5c9z41c4z9402z51774d59c64c
Mechanistic investigation of the Qizao Baoxin Formula in alleviating trastuzumab-induced cardiotoxicity via regulation of ferroptosis: A network pharmacology and molecular docking approach
Liu, X., Gu, Q., Feng, X., Fan, L., Xu, J., Li, C., & li, G. (2026) · Intelligent Pharmacy · doi:10.1016/j.ipha.2026.04.002
Monte-Carlo tree search applied to structure generation
Roucairol, M. (2024) · theses.fr (ABES) · doi:10.70675/a3b9fa7fz7431z42d0z9c49z5b869c8b541b
Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review
Albrijawi, M. T., & Alhajj, R. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01189-3
“…guiding early drug discovery efforts towards molecules amenable to oral administration and reducing attrition, its widespread adoption, sometimes through rigid application, has also sparked debate and a perceived reluctance to explore chemical space that lies outside these guidelines [13, 40].”
Development of tacrine-based multitarget-directed ligands as dual AChE/EGFR inhibitors with neuroprotective activity
Wei, Y., Zhang, A., Qiu, W., Xiong, B., Song, Z., Zheng, N., Wu, Y., Zhang, C., Cao, Z., Wang, X., et al. (2026) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2026.118677
Structure–Activity Relationship Analysis of Macrocyclic Peptide RAS Inhibitors: Spotlight on the Solvent-Exposed Region
Chiyoda, A., Matsuo, A., Yamano, T., & Tanada, M. (2026) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.6c00122 · Cited by 1
Anti- Candida albicans natural products: convergent technologies revolutionizing discovery from bioactivity assessment to targeted mechanisms
Zhang, D., XiaoXing, L., Zhao, X., Yan, Z., Wang, W., & Dai, G. (2026) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2026.2655675
“…et al. 2023; chen and li 2024 (Butler et al. 2014). this limitation has spurred the concept of ‘bRo5 ′ (beyond the Rule of 5) movement, which expands cut-offs for molecular weight and hydrogen bond acceptors, reviving potential for previously discarded molecules (Doak et al. 2014; Shultz 2019).”
Integrated Computational Exploration of Novel KEAP1 Inhibitors Targeting the KEAP1–NRF2 Pathway in Chronic Kidney Disease
Wang, Y., Yang, Y., Xiong, D., & Zaker, R. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-10635-6
“This multi-filter approach is widely recognized in early drug discovery to reduce the likelihood of later-stage attrition by ensuring compliance with diverse structural and ADME criteria, rather than relying on a single empirical rule, which may miss viable candidates [ 53, 54].”
Natural PDE4 inhibitors from plants: A comprehensive review of discovery, structure-activity relationships, and clinical translation potential
Huang, H. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.118960
In Vitro Cytochrome P450 Interaction Profile and ADME Characterisation of Gold(I)–Triphenylphosphine Complexes with 6-Alkoxy-9-deazapurine Ligands
Medvedíková, M., Vančo, J., Trávnı́ček, Z., & Anzenbacher, P. (2026) · Pharmaceutics · doi:10.3390/pharmaceutics18050599
Experimental and computational spectroscopic, docking, and pharmacokinetic investigation of Piconol for optoelectronic and biological applications
Kavitha, R., Suvitha, A. R., Hegazy, R. A., Aldawsari, M. F., Ahmed, M. M., & Anwer, M. K. (2026) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2026.146592
Harnessing QSAR and machine learning for the design of hepatocellular carcinoma inhibitors; experimental validation of the cytotoxicity of new compounds against HepG2 cells
Ali, S., Putra, I. D., Aremu, A. J., Jit-aree, P., Limpachayaporn, P., Boonsombat, J., Ruchirawat, S., Wolschann, P., Maitarad, P., & Rungrotmongkol, T. (2026) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2026.100260
Mitigating Excessive Mitochondrial Fission Through the Development of a Selective Macrocyclic Inhibitor Targeting Fis1/Mid51 Signaling
Zerihun, M., Chakraborty, S., Abita, A., Dey, S., Lopez, I., Shlomo, R. W., Sridhar, M. K. C., Davis, L., Ertracht, O., Atar, S., et al. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c00333
Chemical Composition, Antioxidant, and Anti-Inflammatory Activity of the Antarctic Lichen Leptogium puberulum: A Combination of Metabolomic, In Vitro, and In Silico Approaches
Torres-Benítez, A., Ortega-Valencia, J. E., Salazar, J. R., Monje, K., Ley-Martínez, J. S., Vargas-Arana, G., & Simirgiotis, M. J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27114822
“…fatty acids, such as PHTA, THDA, THHA, and THTA, deviate structurally from conventional drug-like molecules because they possess long aliphatic chains and a high degree of hydroxylation, which increase the number of rotating bonds and hydrogen bond donors beyond classical thresholds [36].”
Chemical space of African natural products: a comparative atlas with approved drugs and Traditional Chinese MedicinePreprint
BELLA, J. Y. T., & Zintchem, A. A. À. (2026) · Research Square · doi:10.21203/rs.3.rs-9904436/v1
Rational design and synthesis of theophylline-based hybrids as bacterial DHFR inhibitors: integrating molecular modelling and ADME prediction with biological validation
Jyoti, Garima, Kumar, N., Megha, Singh, K., Rana, R., Khanna, A., Kaur, I., Kaur, S., & Bedi, P. M. S. (2026) · In Silico Pharmacology · doi:10.1007/s40203-026-00661-0
“However, Lipinski’s rule of five is considered a guideline rather than a strict criteria as several approved drugs fall within the “beyond rule-of-five” (bRo5) chemical space (Lipinski et al. 2012; Doak et al. 2014).”
What Is in a Structure? Cell Permeability and Solubility of Series of Macrocycles and Linear Matched Pairs
Tyagi, M., Poongavanam, V., Zich, S., Lindhagen, M., Asproudis, I., Putra, O. D., Yang, J., Ashworth, Z. J. R., Guadagni, A., Hagemeyer, L. J., et al. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c00830
Self-citation
Convergent synthesis of tri-heterocyclic thiadiazole-triazole-thiazole structural motifs blended with hexanamides: In vitro and in silico explorations as acetylcholinesterase inhibitors
Fatima, Z., Abbasi, M. A., Rehman, A.-U., Siddiqui, S. Z., Raza, H., Shah, S. A. A., Shahid, M., & Kim, S. J. (2026) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2026.146917
A benchmarking-informed structure-based virtual screening strategy targeting Lm-PTR1: Leveraging the Northern African natural products database
Abdelfadil, M., Shaldam, M. A., Elbadawi, M. M., Salem, R., Eldehna, W. M., Bekhit, A. A., Boeckler, F. M., Günther, S., & Ibrahim, T. M. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109503
Atomic degradation: chemical design strategies and immunotherapeutic mechanisms of radio-PROTACs
Varol, T. Ö., & Varol, M. (2026) · Annals of Nuclear Medicine · doi:10.1007/s12149-026-02254-5
“These molecules invariably defy Lipin-ski’s Rule of 5, residing deep within the beyond Rule of 5 (bRo5) chemical space due to high molecular weights (> 1000 Da) and substantial Topological Polar Surface Areas (TPSA > 200 Å 2 ) introduced by the macrocyclic chelator [ 104 ].”
Design, synthesis, and biological evaluation of Combretastatin A4 dimers for the treatment of hepatocellular carcinoma
Wang, Y., Ying, S.-B., Liu, L., Zhang, Q.-D., Pan, Y., Hong, Y., Hu, H.-Y., Liu, Y., Wei, J.-N., Zhang, Z.-J., et al. (2026) · Biochemical Pharmacology · doi:10.1016/j.bcp.2026.118215
Parinari curatellifolia aqueous leaf-bark extract mitigates chronic Plasmodium berghei-induced bone loss in Wistar rats through anti-inflammatory and osteoregenerative mechanisms
Jouonzo, J., Ngueguim, F. T., Kamkumo, R. G., Nangap, J. M. T., Fifen, R. N., Tcheutchoua, Y. C., Simo-Manefeng, A. S., Ambamba, B. D. A., Ngondi, J. L., Ndjakou, B. L., et al. (2026) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2026.122157
Sustainable ionic liquid [CMMIM][BF4]-catalyzed one-pot multicomponent synthesis of spiro pyran-fused ninhydrin derivatives: Molecular docking, DFT, and in silico pharmacokinetics evaluation
Mohurle, S., Kali, V., & Maiti, B. (2026) · Results in Chemistry · doi:10.1016/j.rechem.2026.103625

Authors: Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J.
Affiliations: Uppsala University (SE)
Corresponding author: Kihlberg, Jan
ORCID: Doak, Bradley Croy
Giordanetto, Fabrizio
Kihlberg, Jan
Venue: Chemistry & Biology
Publisher: Elsevier BV
Published: 2014-09
Volume: 21
Issue: 9
Pages: 1115-1142
ISSN: 1074-5521 (ISSN-L)
DOI: 10.1016/j.chembiol.2014.08.013
OpenAlex: W2106605095
Cited by: 835
References: 295
Citations by year: 2026: 101 · 2025: 82 · 2024: 103 · 2023: 111 · 2022: 103 · 2021: 88 · 2020: 65 · 2019: 50 · 2018: 51 · 2017: 27 · 2016: 37 · 2015: 14
FWCI: 2.15
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: public-domain
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 382 d)
Locations: Chemistry & Biology (publishedVersion, public-domain)
PubMed (publishedVersion)
Funders: AstraZeneca
Uppsala Universitet
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords: Rule of 5, oral bioavailability, drug discovery, molecular weight, intramolecular hydrogen bonding, macrocyclization, oral dosage forms, natural products, structure-based drug design, pharmacokinetics, clinical candidates
MeSH terms: Administration, Oral, Biological Products, Pharmaceutical Preparations, Humans, Molecular Weight, Protease Inhibitors, Databases, Factual, Drug Delivery Systems, Macrocyclic Compounds, Drug Discovery

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

Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks