ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support

Author:

Fu Li1ORCID,Shi Shaohua2ORCID,Yi Jiacai3ORCID,Wang Ningning4,He Yuanhang1ORCID,Wu Zhenxing5,Peng Jinfu1,Deng Youchao1,Wang Wenxuan1,Wu Chengkun3ORCID,Lyu Aiping2ORCID,Zeng Xiangxiang6ORCID,Zhao Wentao3,Hou Tingjun5ORCID,Cao Dongsheng1ORCID

Affiliation:

1. Xiangya School of Pharmaceutical Sciences, Central South University , Changsha , Hunan  410013 , P.R. China

2. School of Chinese Medicine, Hong Kong Baptist University , Kowloon , Hong Kong SAR, 999077 , P.R. China

3. School of Computer Science, National University of Defense Technology , Changsha , Hunan  410073 , P.R. China

4. Xiangya Hospital of Central South University , Changsha , Hunan  410008 , P.R. China

5. College of Pharmaceutical Sciences, Zhejiang University , Hangzhou , Zhejiang  310058 , P.R. China

6. Department of Computer Science, Hunan University , Changsha , Hunan  410082 , P.R. China

Abstract

Abstract ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Hunan Provincial Science Fund for Distinguished Young Scholars

Science and Technology Innovation Program of Hunan Province

Natural Science Foundation of Hunan Province

2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund

HKBU Strategic Development Fund

Publisher

Oxford University Press (OUP)

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