Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
Open accessAbstract
Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies.
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Failure of ToxCast and QSARWorld Bioavailability Challenge indicates critical importance of data curation.
“The failure of this92 and QSARWorld Bioavailability Challenge indicates critical importance of data curation.93”
From QSAR without borders (2020) · cites this paper as [93]
Ignoring the data curation step of the modeling workflow will significantly affect the quality of the training data and models derived from them.
“Ignoring the data curation step of the modeling workflow will significantly affect the quality of the training data and models derived from them.93”
From QSAR without borders (2020) · cites this paper as [93]
Many roadblocks and technical issues in statistical data modelling employed in different domains had already been addressed in the QSAR modeling literature, including papers on the impact of errors on QSAR analysis and the importance of data curation for stable and reproducible models.
“Examples include papers on the impact of the errors on QSAR analysis288 and the importance of data curation to achieve stable and reproducible models.93”
From QSAR without borders (2020) · cites this paper as [93]
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Cites
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107 · see citing works
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735 · see citing works
Self-citationConcordance of the toxicity of pharmaceuticals in humans and in animals
Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., et al. (2000) · Regulatory Toxicology and Pharmacology · doi:10.1006/rtph.2000.1399 · Cited by 2,050 · see citing works
Fourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., & Tropsha, A. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900326k · Cited by 162 · see citing works
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164 · see citing works
Self-citationMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationBig data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375 · see citing works
Self-citationIsayev, O., Fourches, D., Muratov, E. N., Oses, C., Rasch, K., Tropsha, A., & Curtarolo, S. (2015) · Chemistry of Materials · doi:10.1021/cm503507h · Cited by 300 · see citing works
Self-citationInformation retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281 · see citing works
Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227 · see citing works
A critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Self-citationGolbraikh, A., Muratov, E., Fourches, D., & Tropsha, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400572x · Cited by 152 · see citing works
Self-citationFourches, D., Pu, D., & Tropsha, A. (2011) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620711794728743 · Cited by 92 · see citing works
Self-citationUniversal Approach for Structural Interpretation of QSAR/QSPR Models
Polishchuk, P. G., Kuz'min, V. E., Artemenko, A. G., & Muratov, E. N. (2013) · Molecular Informatics · doi:10.1002/minf.201300029 · Cited by 76 · see citing works
Self-citation<i>Per Aspera Ad Astra</i> : Application of Simplex Qsar Approach in Antiviral Research
Muratov, E. N., Artemenko, A. G., Varlamova, E. V., Polischuk, P. G., Lozitsky, V. P., Fedchuk, A. S., Lozitska, R. L., Gridina, T. L., Koroleva, L. S., Sil’nikov, V. N., et al. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.194 · Cited by 64 · see citing works
Self-citationA review on the recent applications of deep learning in predictive drug toxicological studies
Sinha, K., Ghosh, N., & Sil, P. C. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00375 · Cited by 64 · see citing works
Machine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works
Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61 · see citing works
Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs
Alves, V. M., Hwang, D., Muratov, E., Sokolsky-Papkov, M., Varlamova, E., Vinod, N., Lim, C., Andrade, C. H., Tropsha, A., & Kabanov, A. (2019) · Science Advances · doi:10.1126/sciadv.aav9784 · Cited by 56 · see citing works
Self-citationReliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Cortés-Ciriano, I., & Bender, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00297 · Cited by 51 · see citing works
Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis
Lin, A. I., Madzhidov, T. I., Klimchuk, O., Nugmanov, R. I., Antipin, I. S., & Varnek, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00319 · Cited by 51 · see citing works
Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
Iovanac, N. C., & Savoie, B. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b01398 · Cited by 41 · see citing works
Luo, M., Wang, X. S., Roth, B. L., Golbraikh, A., & Tropsha, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400460q · Cited by 37 · see citing works
Self-citationConnor, S., Li, T., Qu, Y., Roberts, R. A., & Tong, W. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103938 · Cited by 21 · see citing works
- Authors
- Fourches, D., Muratov, E., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Tropsha, Alexander
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-06-24
- Volume
- 50
- Issue
- 7
- Pages
- 1189-1204
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC2989419
- OpenAlex
- W2057069496
- Cited by
- 832
- References
- 4
- Citations by year
- 2026: 75 · 2025: 60 · 2024: 72 · 2023: 63 · 2022: 52 · 2021: 68 · 2020: 76 · 2019: 64 · 2018: 57 · 2017: 48 · 2016: 41 · 2015: 41 · 2014: 31 · 2013: 33 · 2012: 28
- FWCI
- 27.25
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, public-domain)
- UNC Libraries
- Funders
- National Institutes of Health (GM66940)
- National Institute of General Medical Sciences (R01GM066940, GM66940)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- data cleaning, QSAR models, structural validation, tautomerism, duplicate removal, organometallic compounds, counterions, data quality
- MeSH terms
- Animals, Models, Chemical, Organometallic Compounds, Molecular Structure, Quantitative Structure-Activity Relationship
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