利用人工智能和机器学习来描述蛋白质冠状病毒,纳米生物相互作用,并推进药物发现
1Department of Chemistry, Zonguldak Bülent Ecevit University, 67100 Zonguldak, Türkiye.
Bioengineering (Basel, Switzerland)
|March 28, 2025
概括
了解蛋白质-纳米粒子相互作用对于安全有效的纳米医学至关重要. 数据科学和机器学习 (ML) 提供了强大的工具来预测纳米材料的行为,并提高药物发现.
科学领域:
- 生物医学工程 生物医学工程
- 纳米技术 纳米技术
- 计算生物学 计算生物学
背景情况:
- 蛋白质对生命至关重要,作为药物点,并影响生物医学应用中的纳米粒子 (NP) 行为.
- 在NP上形成的蛋白质冠状体会影响它们的有效性,生物分布,细胞吸收和毒性.
- 传统的纳米医学依赖于实验,但数据科学和机器学习 (ML) 越来越多.
研究的目的:
- 审查数据科学,人工智能 (AI) 和ML在理解纳米生物相互作用中的作用.
- 以突出描述蛋白冠状体的进展,并使用计算方法改善药物发现.
- 讨论纳米信息学在纳米医学中的优势,局限性和未来方向.
主要方法:
- 对纳米信息学,人工智能和ML应用在纳米医学中的当前文献的综述.
- 分析计算模拟和数据科学如何预测纳米材料合成和行为.
- 对AI/ML进行蛋白冠状体征和药物发现的检查.
主要成果:
- 数据科学和ML加速了对纳米材料 (NM) 行为和蛋白质冠状形成的预测.
- 人工智能和机器学习增强了蛋白质冠状病毒的表征,并有助于药物发现过程.
- 纳米信息学整合了计算和实验研究,以评估风险和纳米生物相互作用.
结论:
- 像AI和ML这样的先进计算工具对于优化NP设计以改善治疗结果至关重要.
- 全面的数据集对于提高ML模型在预测NM行为中的准确性至关重要.
- 未来的研究应该集中在深度学习和多模式数据集成上,以进行先进的蛋白质功能预测和安全的NM开发.
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