Machine Learning Strategies When Transitioning between Biological Assays-Reference-Cited by-同舟云学术

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Machine Learning Strategies When Transitioning between Biological Assays

Published:2021-06-21 Issue:7 Volume:61 Page:3722-3733
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Arvidsson McShane Staffan¹^ORCID,Ahlberg Ernst¹²³,Noeske Tobias⁴^ORCID,Spjuth Ola¹^ORCID

Affiliation:

1. Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden

2. Stena Line Scandinavia AB, AI & Data, 405 19 Gothenburg, Sweden

3. Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, 431 50 Gothenburg, Sweden

4. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, 431 50 Gothenburg, Sweden

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c00293

Reference53 articles.

1. High-throughputinvitroprofiling assays: lessons learnt from experiences at Novartis

2. ADME in Drug Discovery

3. Predictive Computational Toxicology to Support Drug Safety Assessment

4. Artificial Intelligence for Drug Toxicity and Safety

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4. Machine learning for small molecule drug discovery in academia and industry;Artificial Intelligence in the Life Sciences;2023-12

5. Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data;Scientific Reports;2022-05-04

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