Admetlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

Jie Dong, Ning Ning Wang, Zhi Jiang Yao, Lin Zhang, Yan Cheng, Defang Ouyang, Aiping Lyu, Dong Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

465 Citations (Scopus)


Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evalua- tion of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at

Original languageEnglish
Article number29
JournalJournal of Cheminformatics
Issue number1
Publication statusPublished - 2018

Scopus Subject Areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Library and Information Sciences

User-Defined Keywords

  • ADMET database
  • ADMETlab
  • Cheminformatics
  • Drug discovery
  • Drug-likeness


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