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      ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

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          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/.

          Graphical Abstract

          Graphical Abstract

          ADMETlab 2.0 assists medicinal chemists in the design and optimization of lead compounds.

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          Most cited references56

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          SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

          To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
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            PubChem in 2021: new data content and improved web interfaces

            Abstract PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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              pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

              Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2021
                24 April 2021
                24 April 2021
                : 49
                : W1
                : W5-W14
                Affiliations
                Xiangya School of Pharmaceutical Sciences, Central South University , Changsha 410013, Hunan, China
                Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang, China
                College of Computer, National University of Defense Technology , Changsha 410073, Hunan, China
                Xiangya School of Pharmaceutical Sciences, Central South University , Changsha 410013, Hunan, China
                Xiangya School of Pharmaceutical Sciences, Central South University , Changsha 410013, Hunan, China
                Tencent Quantum Laboratory , Tencent, Shenzhen 518057, Guangdong, China
                Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University , Changsha 410008, Hunan, China
                Deparment of Computer Science, Hunan University , Changsha 410082, Hunan, China
                College of Computer, National University of Defense Technology , Changsha 410073, Hunan, China
                Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University , Hong Kong SAR, China
                Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University , Changsha 410008, Hunan, China
                Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang, China
                Xiangya School of Pharmaceutical Sciences, Central South University , Changsha 410013, Hunan, China
                Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University , Hong Kong SAR, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 731 8982 4761; Email: oriental-cds@ 123456163.com
                Correspondence may also be addressed to Tingjun Hou. Email: tingjunhou@ 123456zju.edu.cn

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors.

                Author information
                https://orcid.org/0000-0001-7227-2580
                https://orcid.org/0000-0003-3604-3785
                Article
                gkab255
                10.1093/nar/gkab255
                8262709
                33893803
                5fcda541-d980-4268-a043-241394268b7b
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 March 2021
                : 20 March 2021
                : 30 January 2021
                Page count
                Pages: 10
                Funding
                Funded by: Changsha Municipal Natural Science Foundation;
                Award ID: kq2014144
                Funded by: Changsha Science and Technology Bureau;
                Award ID: kq2001034
                Funded by: Key R&D Program of Zhejiang Province;
                Award ID: 2020C03010
                Funded by: National Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 21575128
                Award ID: 81773632
                Funded by: Zhejiang Provincial Natural Science Foundation of China;
                Award ID: LZ19H300001
                Funded by: HKBU Strategic Development Fund;
                Award ID: SDF19-0402-P02
                Categories
                AcademicSubjects/SCI00010
                Web Server issue

                Genetics
                Genetics

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