整合模型信息化药物开发与人工智能:加速制药创新的协同方法
Karthik Raman1,2, Rukmini Kumar3, Cynthia J Musante4
1Centre for Integrative Biology and Systems mEdicine (IBSE), Wadhwani School of Data Science and AI, Indian Institute of Technology (IIT) Madras, Chennai, India.
Clinical and translational science
|January 11, 2025
概括
将人工智能 (AI) 与模型信息化药物开发 (MIDD) 集成,通过优化候选药物选择和治疗策略来加速药物发现. 这种协同作用提高了药物创新的效率和患者的结果.
科学领域:
- 制药科学 制药科学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 制药行业试图提高药物开发效率和患者的治疗结果.
- 基于模型的药物开发 (MIDD) 使用数学模型进行药理动力学和药理动力学模拟.
- 人工智能 (AI) 从大数据中提供先进的模式识别和预测.
研究的目的:
- 探索MIDD和AI在制药研究中的整合.
- 突出结合计算建模与人工智能驱动的洞察力的好处.
- 解决人工智能增强的MIDD的挑战和未来方向.
主要方法:
- 使用数学模型模拟药物过程 (ADME,PK/PD).
- 应用人工智能技术 (机器学习,深度学习,生成人工智能) 来识别和预测模式.
- 进行虚拟试验以优化候选药物和治疗策略.
主要成果:
- 人工智能增强的MIDD优化了药物候选人选择,剂量方案和治疗策略.
- 虚拟试验有助于在开发管道的早期消除候选药物的风险.
- 协同作用加速了药物发现,并使更准确的预测成为可能.
结论:
- MIDD和AI的整合代表了制药创新的变革性范式.
- 模型,数据和验证的标准化对于可靠的结果至关重要.
- 人工智能增强的MIDD有可能开启个性化医学的时代.
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