The DOI matches its Crossref record.
The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies
Abstract
We present a new descriptor named signature based on extended valence sequence. The signature of an atom is a canonical representation of the atom's environment up to a predefined height h. The signature of a molecule is a vector of occurrence numbers of atomic signatures. Two QSAR and QSPR models based on signature are compared with models obtained using popular molecular 2D descriptors taken from a commercially available software (Molconn-Z). One set contains the inhibition concentration at 50% for 121 HIV-1 protease inhibitors, while the second set contains 12865 octanol/water partitioning coefficients (Log P). For both data sets, the models created by signature performed comparable to those from the commercially available descriptors in both correlating the data and in predicting test set values not used in the parametrization. While probing signature's QSAR and QSPR performances, we demonstrates that for any given molecule of diameter D, there is a molecular signature of height h </= D+1, from which any 2D descriptor can be computed. As a consequence of this finding any QSAR or QSPR involving 2D descriptors can be replaced with a relationship involving occurrence number of atomic signatures.
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
Cited by
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
Machine learning of molecular electronic properties in chemical compound space
Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., & Anatole von Lilienfeld, O. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/9/095003 · Cited by 623
Willighagen, E. L., Mayfield, J. W., Alvarsson, J., Berg, A., Carlsson, L., Jeliazkova, N., Kuhn, S., Pluskal, T., Rojas-Chertó, M., Spjuth, O., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0220-4 · Cited by 527
Predicting drug metabolism: experiment and/or computation?
Kirchmair, J., Göller, A. H., Lang, D., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4581 · Cited by 502
Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Perkins, R., & Tong, W. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800038f · Cited by 317
Chemical predictive modelling to improve compound quality
Cumming, J. G., Davis, A. M., Muresan, S., Haeberlein, M., & Chen, H. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4128 · Cited by 311
Ursu, O., Rayan, A., Goldblum, A., & Oprea, T. I. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.52 · Cited by 276
Varnek, A., Fourches, D., Hoonakker, F., & Solov’ev, V. P. (2005) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-005-9008-0 · Cited by 208
Gonzalez-Diaz, H., Arrasate, S., Gomez-SanJuan, A., Sotomayor, N., Lete, E., Besada-Porto, L., & Ruso, J. (2013) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026611313140011 · Cited by 101
A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
Lapins, M., Worachartcheewan, A., Spjuth, O., Georgiev, V., Prachayasittikul, V., Nantasenamat, C., & Wikberg, J. E. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066566 · Cited by 46
Toward Interpretable Machine Learning Models for Materials Discovery
Mikulskis, P., Alexander, M. R., & Winkler, D. A. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900045 · Cited by 43
Structure–reactivity modeling using mixture-based representation of chemical reactions
Polishchuk, P., Madzhidov, T., Gimadiev, T., Bodrov, A., Nugmanov, R., & Varnek, A. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0044-3 · Cited by 27
Metadata
- Authors
- Faulon, J.-L., Visco, D. P., & Pophale, R. S.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-03-26
- Volume
- 43
- Issue
- 3
- Pages
- 707-720
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2213735563
- Cited by
- 232
- References
- 0
- FWCI
- 6.92
- 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.