药物发现中的人工智能:从数据到治疗创新的整合性进步
Mohammad Javad Mehran1,2, Sara Mohammadzadeh1, Mansoor Bolideei3,4
1Department of Medical Biotechnology, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Drug development research
|February 3, 2026
概括
人工智能 (AI) 显著加速药物发现和开发,提高效率和降低成本. 人工智能增强了针对新疗法的目标识别,虚拟查和临床试验设计.
科学领域:
- 药学研究 药学研究
- 计算化学的计算化学
- 生物技术是生物技术.
背景情况:
- 传统的药物发现是昂贵的,耗时的,而且成功率很低.
- 机器学习 (ML),深度学习 (DL) 和强化学习 (RL) 等AI技术提供了解决方案.
研究的目的:
- 审查AI在整个药物发现管道上的影响.
- 突出AI在加速新疗法开发中的作用.
主要方法:
- 人工智能增强了目标识别,虚拟查和新药设计.
- 人工智能改善了毒性预测和药理动力学/药理动力学分析.
- 人工智能通过回复合成预测和反应产量建模优化化学合成.
主要成果:
- 人工智能在临床前和临床阶段显著提高了效率.
- 人工智能改善了患者分层,生物标志物发现和适应性试验设计.
- 人工智能通过多药理学和药物重新定位来促进个性化医疗.
结论:
- 人工智能集成彻底改变了制药研究,加速了救命药物的交付.
- 未来的方向包括解决人工智能的局限性,如数据质量,可解释性和监管接受性.
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