药物发现中的计算毒理学:人工智能在ADMET和毒性预测中的应用
Jiangyan Zhang1, Haolin Li2, Yuncong Zhang3
1School of Pharmacy/School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu City, Sichuan Province, 611137, China.
计算毒理学为药物开发提供了比动物试验更快,更道德的替代方案. 本综述涵盖了ADMET预测平台,数据库和用于毒性评估的AI进步,旨在进行精确的临床前评估.
科学领域:
- 计算毒理学和化学信息学
- 药物的发现和开发和开发.
- 人工智能在毒理学中的应用
背景情况:
- 传统的药物毒性动物试验是昂贵的,缓慢的,并且在道德上有问题.
- 计算毒理学方法正在迅速发展,以解决这些局限性.
- 准确的毒性风险评估对于候选药物成功至关重要.
研究的目的:
- 审查现有的ADMET预测平台和毒理数据库.
- 总结最近人工智能和机器学习在毒性预测方面的进展.
- 讨论计算毒理学的挑战和未来方向.
主要方法:
- 调查了超过20个ADMET预测平台 (规则/统计,ML,基于图表).
- 分类主要的毒理学数据库.
- 审查了AI/ML在预测各种毒性 (急性,器官特异性,致癌性) 的应用.
- 探索了生成建模,可解释性,网络毒理学和法学士.
主要成果:
- 该领域正在向多端点联合建模和多式联运特征迈进.
- 生成式建模和可解释性框架提高了预测的准确性和可信性.
- 网络毒理学和LLM显示了TCM安全性和文献挖掘的潜力.
结论:
- 计算毒理学,特别是人工智能驱动的方法,提供了高效和精确的临床前毒性评估.
- 处理数据质量,可解释性和因果推理是未来进步的关键.
- 整合多学科数据和开发特定领域的LLM将推动该领域的发展.
更多相关视频
16:02Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
Published on: February 10, 2023
09:01A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
Published on: March 14, 2019
相关概念视频
Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
Drug Discovery: Overview
Mutagenicity and Carcinogenicity
Toxic Reactions: Overview
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...
