The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Binding Site Detection and Druggability Index from First Principles
Abstract
In drug discovery, it is essential to identify binding sites on protein surfaces that drug-like molecules could exploit to exert a biological effect. Both X-ray crystallography and NMR experiments have demonstrated that organic solvents bind precisely at these locations. We show that this effect is reproduced using molecular dynamics with a binary solvent. Furthermore, analysis of the simulations give direct access to interaction free energies between the protein and small organic molecules, which can be used to detect binding sites and to predict the maximal affinity that a drug-like molecule could attain for them. On a set of pharmacologically relevant proteins, we obtain good predictions for druggable sites as well as for protein-protein and low affinity binding sites. This is the first druggability index not based on surface descriptors and, being independent of a training set, is particularly indicated to study unconventional targets such as protein-protein interactions or allosteric binding sites.
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
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
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305
Small-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
Hot spots—A review of the protein–protein interface determinant amino‐acid residues
Moreira, I. S., Fernandes, P. A., & Ramos, M. J. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21396 · Cited by 760
On Outliers and Activity CliffsWhy QSAR Often Disappoints
Maggiora, G. M. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060117s · Cited by 728
Ding, K., Lu, Y., Nikolovska-Coleska, Z., Wang, G., Qiu, S., Shangary, S., Gao, W., Qin, D., Stuckey, J., Krajewski, K., et al. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051122a · Cited by 702
Fasan, R., Dias, R. L. A., Moehle, K., Zerbe, O., Obrecht, D., Mittl, P. R. E., Grütter, M. G., & Robinson, J. A. (2006) · ChemBioChem · doi:10.1002/cbic.200500452 · Cited by 129
Cited by
Next generation 3D pharmacophore modeling
Schaller, D., Šribar, D., Noonan, T., Deng, L., Nguyen, T. N., Pach, S., Machalz, D., Bermudez, M., & Wolber, G. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1468 · Cited by 269
The Free Energy Landscape of Small Molecule Unbinding
Huang, D., & Caflisch, A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002002 · Cited by 116
“Only recently, explicit solvent molecular dynamics (MD) simulations have been used to investigate the binding of small fragments to proteins at atomistic level of detail, which is very helpful for the design of small-molecule inhibitors [5,6,7,8].”
BackgroundYang, C.-Y., & Wang, S. (2011) · ACS Medicinal Chemistry Letters · doi:10.1021/ml100276b · Cited by 57
Druggable Orthosteric and Allosteric Hot Spots to Target Protein-protein Interactions
Ma, B., & Nussinov, R. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113199990073 · Cited by 45
Nordquist, E. B., Zhao, M., Kumar, A., & MacKerell, A. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01189 · Cited by 15
Metadata
- Authors
- Seco, J., Luque, F. J., & Barril, X.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-03-18
- Volume
- 52
- Issue
- 8
- Pages
- 2363-2371
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W1995206879
- Cited by
- 239
- References
- 8
- FWCI
- 16.74
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Binding Site Detection and Druggability Index from First Principles. https://app.cochat.ai/papers/10.1021/jm801385d
- The paper
- Seco, J., Luque, F. J., & Barril, X. (2009). Binding Site Detection and Druggability Index from First Principles. Journal of Medicinal Chemistry. https://doi.org/10.1021/jm801385d
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.
