Abstract

Abstract Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Keywords

ADMEIn silicoToxicityComputer scienceComputational biologyBiologyPharmacokineticsWeb serverPharmacologyThe InternetWorld Wide WebBiochemistryChemistry

MeSH Terms

Drug-Related Side Effects and Adverse ReactionsInternetPharmaceutical PreparationsPharmacokineticsPhthalazinesPiperazinesSoftware

Affiliated Institutions

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Publication Info

Year
2021
Type
article
Volume
49
Issue
W1
Pages
W5-W14
Citations
2290
Access
Closed

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Social media, news, blog, policy document mentions

Citation Metrics

2290
OpenAlex
393
Influential
2145
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Cite This

Guo‐Li Xiong, Zhenhua Wu, Jiacai Yi et al. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research , 49 (W1) , W5-W14. https://doi.org/10.1093/nar/gkab255

Identifiers

DOI
10.1093/nar/gkab255
PMID
33893803
PMCID
PMC8262709

Data Quality

Data completeness: 90%