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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database.

Jie Dong1,2,3, Ning-Ning Wang1, Zhi-Jiang Yao1

  • 1Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.

Journal of Cheminformatics
|June 27, 2018
PubMed
Summary

ADMETlab is a new web platform for rapid chemical evaluation. It aids drug discovery by predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties early.

Keywords:
ADMETADMET databaseADMETlabCheminformaticsDrug discoveryDrug-likeness

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Area of Science:

  • Computational chemistry
  • Drug discovery informatics

Background:

  • Pharmaceutical R&D faces high failure rates due to efficacy and safety issues.
  • Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties are critical for drug success.
  • Early and rapid ADMET evaluation is essential to mitigate R&D failures.

Purpose of the Study:

  • To develop a web-based platform, ADMETlab, for systematic chemical ADMET evaluation.
  • To provide tools for early drug-likeness assessment and virtual screening.

Main Methods:

  • Development of ADMETlab, a web platform utilizing a database of 288,967 entries.
  • Implementation of four functional modules for drug-likeness analysis, ADMET endpoint prediction, systematic evaluation, and database searching.
  • Utilized Django framework in Python for platform development.

Main Results:

  • ADMETlab offers six drug-likeness analyses and predicts 31 ADMET endpoints across key categories (basic properties, absorption, distribution, metabolism, elimination, toxicity).
  • The platform facilitates systematic evaluation and database/similarity searching of chemical structures.
  • Provides comprehensive ADMET data for 288,967 entries.

Conclusions:

  • ADMETlab is a valuable tool for early drug-likeness evaluation and rapid ADMET virtual screening.
  • The platform can accelerate drug discovery by enabling efficient filtering and prioritization of chemical compounds.
  • Freely accessible at http://admet.scbdd.com/ to support the pharmaceutical research community.