发展性毒性与可解释的人工智能相遇的地方:最先进的技术和前景
Maria Vittoria Togo1, Fabrizio Mastrolorito1, Angelica Orfino1
1Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy.
Expert opinion on drug metabolism & toxicology
|December 23, 2023
概括
人工智能 (AI) 正在推进预测毒理学,为发育毒性 (Dev Tox) 提供非测试方法. 可解释的人工智能 (XAI) 为监管使用提供了可靠,透明的模型.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 监管科学 监管科学
背景情况:
- 预测毒理学越来越多地使用人工智能 (AI) 来开发非测试方法.
- 发育性毒性 (Dev Tox) 是对母亲和儿童健康的关键人类健康终点.
- 伦理问题和经济成本推动了对替代测试策略的需求.
研究的目的:
- 审查现有的发展毒性 (Dev Tox) 预测方法.
- 突出在Dev Tox.中新方法方法 (NAMs) 的优势.
- 强调可解释的人工智能 (XAI) 在创建可靠和透明的预测模型中的作用.
主要方法:
- 审查当前的Dev Tox预测策略.
- 专注于新方法方法论 (NAMs) 的应用.
- 探索可解释的人工智能 (XAI) 用于模型开发.
主要成果:
- XAI有助于构建可解释和透明的预测模型.
- XAI的模型与国际监管机构的建议保持一致.
- XAI解决了Dev Tox.中有限的高质量数据所带来的挑战.
结论:
- 可解释的人工智能 (XAI) 是一个有前途的发展毒性 (Dev Tox) 预测方法.
- XAI提高了模型的可靠性和透明度,支持科学和监管决策.
- 在NAM中采用XAI对于推进毒理学中非测试方法至关重要.
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