Perspectives on the use of machine learning for ADME prediction at AstraZeneca-Reference-Cited by-同舟云学术

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Perspectives on the use of machine learning for ADME prediction at AstraZeneca

Published:2024-08-08 Issue:7 Volume:54 Page:368-378
ISSN:0049-8254
Container-title:Xenobiotica
language:en
Short-container-title:Xenobiotica

Author:

Gawehn Erik¹,Greene Nigel²^ORCID,Miljković Filip³,Obrezanova Olga⁴,Subramanian Vigneshwari¹,Trapotsi Maria-Anna⁴,Winiwarter Susanne⁵

Affiliation:

1. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden

2. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Waltham, MA, USA

3. Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden

4. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom

5. Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00498254.2024.2352598

Reference37 articles.

1. ACD/Labs Software; Advanced Chemistry Development. 2015.

2. Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines

3. ClogP version 4.3; Pomona College and BioByte.

4. Support-vector networks

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