医学的未来:人工智能和机器学习驱动药物发现的进步
Divya D Patel1, Ruchi S Pathak1, Kaushika S Patel2
1Department of Pharmaceutical Chemistry, L.J. Institute of Pharmacy, LJ University, Ahmedabad 382 210, India.
Current topics in medicinal chemistry
|April 10, 2025
概括
计算机辅助药物设计 (CADD) 使用AI和ML加速药物发现. 这些先进的方法提高了开发个性化治疗方法的准确性和效率.
科学领域:
- 计算化学和药理学计算化学和药理学
- 人工智能在药物发现中的作用
背景情况:
- 传统的药物设计依赖于经验方法.
- 计算机辅助药物设计 (CADD) 提供了一个更安全,更快,更具成本效益的系统.
- 基于结构的药物设计 (SBDD) 和基于连接体的药物设计 (LBDD) 等CADD技术在药物发现中至关重要.
研究的目的:
- 突出CADD在药物设计中的演变和影响.
- 强调人工智能 (AI) 和机器学习 (ML) 在推进CADD中的作用.
- 展示包括深度学习 (DL) 在内的AI/ML如何增强药物发现和开发.
主要方法:
- 使用SBDD,LBDD和Pharmacophore建模等CADD技术.
- 整合AI和ML,包括DL,用于处理复杂的数据并改进药物向相互作用预测.
- 采用先进的技术,如生理学基础的药理动力学 (PBPK) 建模和纳米定量结构-活性关系 (纳米-QSAR),以了解药物行为和预测毒性.
主要成果:
- AI/ML集成显著提高了CADD的有效性和准确性.
- DL有效地处理复杂的非线性数据,减少复杂性,提高资源利用率.
- 先进的建模技术提高了对药物行为的理解,并预测了潜在的纳米材料毒性,从而导致更安全的治疗干预措施.
结论:
- 人工智能/ML方法正在彻底改变医疗保健,改善诊断,临床研究和重症监护.
- 在药物开发中应用AI/ML导致更准确,更有效,更针对患者的治疗策略.
- 人工智能/ML技术的持续进步承诺在药物开发中提供更高的准确性,有效性和个性化的反应.
关键词:
机器学习 (ML).机器学习.相关概念视频
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