从药物向相互作用的角度阐明人工智能在药物开发中的作用
Boyang Wang1, Tingyu Zhang1, Qingyuan Liu1
1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing, 100084, China.
Journal of pharmaceutical analysis
|March 18, 2025
概括
人工智能 (AI) 通过使用机器学习,深度学习 (CNN,GCN,变压器) 和基于网络的模型来预测药物向关联来加速药物开发. 这些人工智能工具提高了发现新疗法的准确性和效率.
科学领域:
- 生物医学是生物医学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物开发是一个关键的生物医学挑战.
- 大数据时代和人工智能的进步正在彻底改变药物发现.
- 人工智能辅助的药物标预测是一个关键的新兴趋势.
研究的目的:
- 系统地审查和评估用于预测药物和药物组合目标关联的AI算法.
- 突出各种AI模型的理论框架,优势和局限性.
- 为生物医学研究中的AI模型和算法提供建议.
主要方法:
- 审查人工智能算法,包括传统的机器学习 (ML),基于网络的模型和深度学习架构 (CNN,GCN,变压器).
- 评估AI模型提取特征,模型交互和预测结果的能力.
- 分析AI在处理复杂的生物数据和大型数据集中的作用.
主要成果:
- CNNs擅长识别药物向相互作用的空间模式和分子特征.
- 通过关系数据,GCN提供了对分子相互作用的洞察力.
- 变压器通过捕捉生物序列中的复杂依赖关系来提高预测准确度.
- 基于网络的模型整合了各种数据,以系统地查看.
- 传统的ML有效地处理大数据集,以提高预测准确度.
结论:
- 人工智能驱动的方法正在改变药物标预测和推进个性化治疗.
- 人工智能显著加快了药物发现和开发的速度.
- 人工智能为生物医学和药物发现研究人员提供了强大的工具.
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