The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Is There a Difference between Leads and Drugs? A Historical Perspective
Abstract
To be considered for further development, lead structures should display the following properties: (1) simple chemical features, amenable for chemistry optimization; (2) membership to an established SAR series; (3) favorable patent situation; and (4) good absorption, distribution, metabolism, and excretion (ADME) properties. There are two distinct categories of leads: those that lack any therapeutic use (i.e., "pure" leads), and those that are marketed drugs themselves but have been altered to yield novel drugs. We have previously analyzed the design of leadlike combinatorial libraries starting from 18 lead and drug pairs of structures (S. J. Teague et al. Angew. Chem., Int. Ed. Engl. 1999, 38, 3743-3748). Here, we report results based on an extended dataset of 96 lead-drug pairs, of which 62 are lead structures that are not marketed as drugs, and 75 are drugs that are not presumably used as leads. We examined the following properties: MW (molecular weight), CMR (the calculated molecular refractivity), RNG (the number of rings), RTB (the number of rotatable bonds), the number of hydrogen bond donors (HDO) and acceptors (HAC), the calculated logarithm of the n-octanol/water partition (CLogP), the calculated logarithm of the distribution coefficient at pH 7.4 (LogD(74)), the Daylight-fingerprint druglike score (DFPS), and the property and pharmacophore features score (PPFS). The following differences were observed between the medians of drugs and leads: DeltaMW = 69; DeltaCMR = 1.8; DeltaRNG = DeltaHAC =1; DeltaRTB = 2; DeltaCLogP = 0.43; DeltaLogD(74) = 0.97; DeltaHDO = 0; DeltaDFPS = 0.15; DeltaPPFS = 0.12. Lead structures exhibit, on the average, less molecular complexity (less MW, less number of rings and rotatable bonds), are less hydrophobic (lower CLogP and LogD(74)), and less druglike (lower druglike scores). These findings indicate that the process of optimizing a lead into a drug results in more complex structures. This information should be used in the design of novel combinatorial libraries that are aimed at lead discovery.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484
A Scoring Scheme for Discriminating between Drugs and Nondrugs
Sadowski, J., & Kubinyi, H. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm9706776 · Cited by 392
Cited by
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403
Self-citationSmall-molecule inhibitors of protein–protein interactions: progressing towards the dream
Arkin, M. R., & Wells, J. A. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1343 · Cited by 1,705
Impact of high-throughput screening in biomedical research
Macarron, R., Banks, M. N., Bojanic, D., Burns, D. J., Cirovic, D. A., Garyantes, T., Green, D. V. S., Hertzberg, R. P., Janzen, W. P., Paslay, J. W., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3368 · Cited by 1,364
The role of ligand efficiency metrics in drug discovery
Hopkins, A. L., Keserü, G. M., Leeson, P. D., Rees, D. C., & Reynolds, C. H. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4163 · Cited by 1,181
Self-citationHalogen 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
Drug research: myths, hype and reality
Kubinyi, H. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1156 · Cited by 348
Ursu, O., Rayan, A., Goldblum, A., & Oprea, T. I. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.52 · Cited by 276
Self-citationExploring new targets and chemical space with affinity selection-mass spectrometry
Prudent, R., Annis, D. A., Dandliker, P. J., Ortholand, J.-Y., & Roche, D. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-00229-2 · Cited by 104
Badapple: promiscuity patterns from noisy evidence
Yang, J. J., Ursu, O., Lipinski, C. A., Sklar, L. A., Oprea, T. I., & Bologa, C. G. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0137-3 · Cited by 103
Self-citationColoring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96
Pharmacokinetically Based Mapping Device for Chemical Space Navigation
Oprea, T. I., Zamora, I., & Ungell, A.-L. (2002) · Journal of Combinatorial Chemistry · doi:10.1021/cc010093w · Cited by 77
Self-citationIncreasing the efficiency of ligands for FK506-binding protein 51 by conformational control
Wang, Y., Kirschner, A., Fabian, A.-K., Gopalakrishnan, R., Kress, C., Hoogeland, B., Koch, U., Kozany, C., Bracher, A., & Hausch, F. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400087k · Cited by 60
How do metabolites differ from their parent molecules and how are they excreted?
Kirchmair, J., Howlett, A., Peironcely, J. E., Murrell, D. S., Williamson, M. J., Adams, S. E., Hankemeier, T., van Buren, L., Duchateau, G., Klaffke, W., et al. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300487z · Cited by 43
Protein-Protein Interactions as Drug Targets
Wendt, M. D. (2012) · Topics in Medicinal Chemistry · doi:10.1007/978-3-642-28965-1_1 · Cited by 9
Early ADME/T Predictions: Toy or Tool?
Tetko, I. V., & Oprea, T. I. (2008) · Chemoinformatics Approaches to Virtual Screening · doi:10.1039/9781847558879-00240 · Cited by 5
Self-citationLarge Language Model Agent for Modular Task Execution in Drug Discovery
Ock, J., Meda, R. S., Badrinarayanan, S., Aluru, N. S., Chandrasekhar, A., & Barati Farimani, A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02454 · Cited by 5
Metadata
- Authors
- Oprea, T. I., Davis, A. M., Teague, S. J., & Leeson, P. D.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-07-20
- Volume
- 41
- Issue
- 5
- Pages
- 1308-1315
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W1975293246
- Cited by
- 769
- References
- 3
- FWCI
- 35.07
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
