The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Prediction of Physicochemical Parameters by Atomic Contributions
Abstract
We present a new atom type classification system for use in atom-based calculation of partition coefficient (log P ) and molar refractivity (MR) designed in part to address published concerns of previous atomic methods. The 68 atomic contributions to log P have been determined by fitting an extensive training set of 9920 molecules, with r 2 = 0.918 and σ = 0.677. A separate set of 3412 molecules was used for the determination of contributions to MR with r 2 = 0.997 and σ = 1.43. Both calculations are shown to have high predictive ability.
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
Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates
Roughley, S. D., & Jordan, A. M. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200187y · Cited by 2,725
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
Jaeger, S., Fulle, S., & Turk, S. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00616 · Cited by 758
Property-Based Design: Optimization of Drug Absorption and Pharmacokinetics
van de Waterbeemd, H., Smith, D. A., Beaumont, K., & Walker, D. K. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm000407e · Cited by 599
Predicting physical properties of ionic liquids
Deetlefs, M., Seddon, K. R., & Shara, M. (2006) · Phys. Chem. Chem. Phys. · doi:10.1039/b513453f · Cited by 411
Edmondson, S. D., Yang, B., & Fallan, C. (2019) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2019.04.030 · Cited by 422
Binding MOAD (Mother Of All Databases)
Hu, L., Benson, M. L., Smith, R. D., Lerner, M. G., & Carlson, H. A. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20512 · Cited by 328
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling
Martins, I. F., Teixeira, A. L., Pinheiro, L., & Falcao, A. O. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300124c · Cited by 331
FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects
Lagorce, D., Sperandio, O., Galons, H., Miteva, M. A., & Villoutreix, B. O. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-396 · Cited by 263
An artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99
GFscore: a general nonlinear consensus scoring function for high-throughput docking.
Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., & Morelli, X. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600758 · Cited by 51
Structure-Based Drug Design with a Deep Hierarchical Generative Model
Weller, J. A., & Rohs, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01193 · Cited by 47
QSAR Models for the Analysis of Bioresponse Data from Combinatorial Libraries of Biomaterials
Smith, J. R., Kholodovych, V., Knight, D., Welsh, W. J., & Kohn, J. (2005) · QSAR & Combinatorial Science · doi:10.1002/qsar.200420062 · Cited by 39
Language models for the prediction of SARS-CoV-2 inhibitors
Blanchard, A. E., Gounley, J., Bhowmik, D., Chandra Shekar, M., Lyngaas, I., Gao, S., Yin, J., Tsaris, A., Wang, F., & Glaser, J. (2022) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420221121804 · Cited by 30
Are there physicochemical differences between allosteric and competitive ligands?
Smith, R. D., Lu, J., & Carlson, H. A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005813 · Cited by 27
Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer
Liao, Z., Xie, L., Mamitsuka, H., & Zhu, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac814 · Cited by 24
Sako, M., Yasuo, N., & Sekijima, M. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01385 · Cited by 21
Voronov, G., Lightheart, R., Frandsen, A., Bargh, B., Haynes, S. E., Spencer, E., Schoenhardt, K. E., Davidson, C., Schaum, A., Macherla, V. R., et al. (2022) · doi:10.1101/2022.10.09.511482 · Cited by 18
Metadata
- Authors
- Wildman, S. A., & Crippen, G. M.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1999-08-19
- Volume
- 39
- Issue
- 5
- Pages
- 868-873
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2066273100
- Cited by
- 1,464
- References
- 1
- FWCI
- 10.00
- 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.
