The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties
Abstract
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key roles in the discovery/development of drugs, pesticides, food additives, consumer products, and industrial chemicals. This information is especially useful when to conduct environmental and human hazard assessment. The most critical rate limiting step in the chemical safety assessment workflow is the availability of high quality data. This paper describes an ADMET structure-activity relationship database, abbreviated as admetSAR. It is an open source, text and structure searchable, and continually updated database that collects, curates, and manages available ADMET-associated properties data from the published literature. In admetSAR, over 210,000 ADMET annotated data points for more than 96,000 unique compounds with 45 kinds of ADMET-associated properties, proteins, species, or organisms have been carefully curated from a large number of diverse literatures. The database provides a user-friendly interface to query a specific chemical profile, using either CAS registry number, common name, or structure similarity. In addition, the database includes 22 qualitative classification and 5 quantitative regression models with highly predictive accuracy, allowing to estimate ecological/mammalian ADMET properties for novel chemicals. AdmetSAR is accessible free of charge at http://www.admetexp.org.
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Cites
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
Zhu, H., Martin, T. M., Ye, L., Sedykh, A., Young, D. M., & Tropsha, A. (2009) · Chemical Research in Toxicology · doi:10.1021/tx900189p · Cited by 287
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
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
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“For this different web-based tools have been developed such as LimTox (http:// limtox. bioin fo. cnio. es/) [321], pkCSM (http:// biosig. unime lb. edu. au/ pkcsm/) [322], admetSAR (http:// lmmd. ecust. edu. cn/ admet sar2/) [323], and Toxtree (http:// toxtr ee. sourc eforge. net/) [324].”
MethodBlockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281
Temozolomide and Other Alkylating Agents in Glioblastoma Therapy
Strobel, H., Baisch, T., Fitzel, R., Schilberg, K., Siegelin, M. D., Karpel-Massler, G., Debatin, K.-M., & Westhoff, M.-A. (2019) · Biomedicines · doi:10.3390/biomedicines7030069 · Cited by 279
Metadata
- Authors
- Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W., & Tang, Y.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-11-01
- Volume
- 52
- Issue
- 11
- Pages
- 3099-3105
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2014858249
- Cited by
- 2,222
- References
- 3
- FWCI
- 30.54
- Type
- journal article
- Language
- en
- Open access
- closed
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