综合机器学习和深度学习驱动人工智能模型对药理动力学和毒理动力学预测及其应用的审查
Malarvannan M1, Monohar S1, Sanskruti Sitaram Kate1
1Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Kolkata, West Bengal, India.
Drug metabolism and disposition: the biological fate of chemicals
|February 28, 2026
概括
混合人工智能 (AI) 模型通过改善吸收,分布,新陈代谢,分泌和毒性 (ADMET) 预测来增强药物发现. 这些先进的AI方法减少了开发时间和成本,加速了新化学实体的识别.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能 (AI) 正在彻底改变药物发现,混合模型将深度学习 (DL) 和机器学习 (ML) 结合起来,显示出有前途.
- 传统的ML和DL模型难以准确预测吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性.
- 提高ADMET预测的准确性仍然是传统药物开发中的一个重大挑战.
研究的目的:
- 审查人工智能在药物发现中的转变,从传统的DL / ML到混合学习模型.
- 检查药物研究中混合人工智能模型的系统趋势和优势.
- 突出使用混合人工智能和多模拟技术的新ADMET软件的作用.
主要方法:
- 对人工智能驱动的药物发现转型进行系统审查,重点关注混合学习模型.
- 分析传统的DL和ML方法与混合AI模型对ADMET预测的分析.
- 评估与混合人工智能集成的多模拟技术,以提高预测.
主要成果:
- 混合人工智能模型显示效率提高,药物开发时间和成本降低,与传统的ML和DL相比,成功率提高.
- 基于混合AI的新ADMET软件增强了药理动力学-药理动力学预测和ADMET终点准确性.
- 混合人工智能通过提高预测可靠性来加快新化学实体的发现.
结论:
- 混合人工智能模型代表了药物发现的重大进步,提供了卓越的ADMET预测准确性.
- 混合人工智能和多模拟技术的整合对于未来的制药研发至关重要.
- 持续开发人工智能驱动的预测模型,包括混合方法,将加快新疗法的交付.
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