AlzyFinder:一种机器学习驱动的平台,用于基于基的虚拟查和网络药理学
Jessica Valero-Rojas1,2, Camilo Ramírez-Sánchez3, Laura Pacheco-Paternina1
1Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4070386, Chile.
AlzyFinder平台通过人工智能驱动的虚拟查和网络药理学加速阿尔茨海默病 (AD) 药物发现. 这种免费的工具可以识别潜在的多目标连接体,有助于开发新的AD治疗方法.
科学领域:
- 计算化学和药理学计算化学和药理学
- 生物信息学和化学信息学
- 神经科学和药物发现
背景情况:
- 阿尔茨海默病 (AD) 药物开发面临的挑战是由于其复杂的性质.
- 识别有效的多目标配体对于AD疗法至关重要.
- 现有的工具缺乏AD的综合虚拟查和网络药理学.
研究的目的:
- 介绍AlzyFinder,这是一个免费的,基于网络的阿尔茨海默病研究平台.
- 为了实现并行基于连接体的虚拟查和网络药理学分析.
- 加速对AD的潜在多目标配体的识别和分析.
主要方法:
- 开发机器学习模型 (XGBoost,Optuna) 用于基于联体的虚拟选.
- 使用活性,非活性和诱分子与平衡准确性,精度和F1得分等指标验证模型.
- 整合软投票整体方法,以精细分类和全面分析交互数据.
主要成果:
- AlzyFinder成功地选了五种已知的活性化合物与关键的AD目标对抗,预测它们是真正的阳性 (>0.70概率).
- 该平台在识别潜在的治疗化合物方面表现出高精度.
- 网络药理学分析提供了对全身药物作用和相互作用的见解.
结论:
- AlzyFinder是第一个开放式访问平台,结合了虚拟查和AD的网络药理学.
- 该平台增强了药物-蛋白质和蛋白质-蛋白质相互作用的可视化和分析.
- AlzyFinder是加速阿尔茨海默病治疗开发的宝贵资源.
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