克服机器学习应用于毒性预测的障碍

IF 3.9 3区医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Expert Opinion on Drug Metabolism & Toxicology Pub Date : 2023-12-13 DOI:10.1080/17425255.2023.2294939

Abdallah Abou Hajal, Ahmad Z. Al Meslamani

引用次数: 0

摘要

发表于《药物代谢与毒理学专家意见》（2023 年，提前出版）

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Overcoming barriers to machine learning applications in toxicity prediction

Published in Expert Opinion on Drug Metabolism & Toxicology (Ahead of Print, 2023)

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来源期刊

Expert Opinion on Drug Metabolism & Toxicology 医学-生化与分子生物学

CiteScore

7.90

自引率

2.30%

发文量

审稿时长

4-8 weeks

期刊介绍： Expert Opinion on Drug Metabolism & Toxicology (ISSN 1742-5255 [print], 1744-7607 [electronic]) is a MEDLINE-indexed, peer-reviewed, international journal publishing review articles on all aspects of ADME-Tox. Each article is structured to incorporate the author’s own expert opinion on the scope for future development. The Editors welcome: Reviews covering metabolic, pharmacokinetic and toxicological issues relating to specific drugs, drug-drug interactions, drug classes or their use in specific populations; issues relating to enzymes involved in the metabolism, disposition and excretion of drugs; techniques involved in the study of drug metabolism and toxicology; novel technologies for obtaining ADME-Tox data. Drug Evaluations reviewing the clinical, toxicological and pharmacokinetic data on a particular drug. The audience consists of scientists and managers in the pharmaceutical industry, pharmacologists, clinical toxicologists and related professionals.