The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program
Abstract
This article provides a systematic study of several important parameters of the Associative Neural Network (ASNN), such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm. The performance of the different methods is assessed with several training/test sets used to predict lipophilicity of chemical compounds. The Spearman rank-order correlation coefficient and Parzen-window regression methods provide the best performance of the algorithm. If additional user data is available, an improved prediction of lipophilicity of chemicals up to 2-5 times can be calculated when the appropriate smoothing parameters for the neural network are selected. The detected best combinations of parameters and strategies are implemented in the ALOGPS 2.1 program that is publicly available at http://www.vcclab.org/lab/alogps.
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
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538
Cited by
Rayne, S., & Forest, K. (2009) · Journal of Environmental Science and Health, Part A · doi:10.1080/10934520903139811 · Cited by 475
A solvent having switchable hydrophilicity
Jessop, P. G., Phan, L., Carrier, A., Robinson, S., Dürr, C. J., & Harjani, J. R. (2010) · Green Chemistry · doi:10.1039/b926885e · Cited by 329
Tertiary amine solvents having switchable hydrophilicity
Jessop, P. G., Kozycz, L., Rahami, Z. G., Schoenmakers, D., Boyd, A. R., Wechsler, D., & Holland, A. M. (2011) · Green Chemistry · doi:10.1039/c0gc00806k · Cited by 260
Design and evaluation of switchable-hydrophilicity solvents
Vanderveen, J. R., Durelle, J., & Jessop, P. G. (2014) · Green Chem. · doi:10.1039/c3gc42164c · Cited by 251
A renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235
Self-citationPROTAC-DB: an online database of PROTACs
Weng, G., Shen, C., Cao, D., Gao, J., Dong, X., He, Q., Yang, B., Li, D., Wu, J., & Hou, T. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa807 · Cited by 219
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102
“CODE program Inductive (3D) 34 Descriptors based on inductive and steric effects of atoms Chemaxon (3D) A subset of descriptors calculated by Chemaxon (www.chemaxon.com) module in OCHEM Mera and Mesry (3D) 35 3D descriptors of molecules GSFrag 35 Descriptors calculated by GSFRAG program (the occurrence numbers of certa…”
MethodSelf-citationA novel near-infrared fluorescent probe for sensitive detection of β-galactosidase in living cells
Zhang, J., Li, C., Dutta, C., Fang, M., Zhang, S., Tiwari, A., Werner, T., Luo, F.-T., & Liu, H. (2017) · Analytica Chimica Acta · doi:10.1016/j.aca.2017.02.039 · Cited by 101
Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86
Self-citationEarly 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-citation
Metadata
- Authors
- Tetko, I. V., & Tanchuk, V. Y.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-08-09
- Volume
- 42
- Issue
- 5
- Pages
- 1136-1145
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W1541037889
- Cited by
- 617
- References
- 2
- FWCI
- 13.28
- 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.
