用于药物向相互作用预测的机器学习:对模型,挑战和计算策略的全面审查
Bilal Ahmad1, Khmaies Ouahada1, Habib Hamam1,2
1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Corner Kingsway, Johannesburg, 2092, Gauteng, South Africa.
人工智能 (AI) 和机器学习 (ML) 正在彻底改变药物向相互作用 (DTI) 的预测. 这些先进的计算方法加速了药物发现,降低了开发新药的成本和时间表.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物发现是一个漫长,昂贵和高风险的过程.
- 药物向相互作用 (DTI) 的实验性识别是由于时间和成本的限制而造成的重大瓶.
研究的目的:
- 审查人工智能 (AI) 和机器学习 (ML) 对DTI预测的变革性影响.
- 探索AI/ML技术,以提高药物发现的效率和准确性.
主要方法:
- 详细检查AI/ML技术用于DTI预测.
- 讨论数据表示,特征工程和交互特征.
- 学习范式的概述,包括监督学习,图形神经网络 (GNN),深度学习 (DL) 和混合模型.
主要成果:
- 人工智能/ML方法为DTI预测提供了更加准确,可扩展和可解释的解决方案.
- 这些技术有可能显著减少药物开发时间和成本.
- 先进的模型改进了关键阶段,如标识,药物重新定位和多药理学分析.
结论:
- 人工智能/ML对于加速药物开发管道至关重要.
- 这些计算方法对于推进精准医学至关重要.
- 该审查强调了AI/ML克服DTI预测传统局限性的能力.
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