在QSPR模型和CRITIC驱动的MCDM方法中使用基于度的指数对多囊卵巢综合征 (PCOS) 药物进行排名
Muhammad Waheed Rasheed1, Abid Mahboob2, Laiba Amin3
1Department of Mathematics, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan. waheedrasheed461@gmail.com.
本研究探讨了使用图形理论和多标准决策的多囊性卵巢综合征 (PCOS) 治疗方法. 奥尔利斯塔特成为PCOS管理的有希望的候选药物,突出了个性化医学的拓指数.
科学领域:
- 内分泌学和计算化学
- 专注于多囊性卵巢综合征 (PCOS) 和使用先进的计算方法发现药物.
背景情况:
- 多囊卵巢综合征 (PCOS) 影响全球8-13%的生育年龄妇女,源于遗传,荷尔蒙和生活方式因素.
- 目前的PCOS治疗需要优化疗效和个性化患者治疗方法.
研究的目的:
- 用计算方法调查PCOS原因并评估治疗疗效.
- 预测PCOS药物的化学结构和特性.
- 使用多标准决策 (MCDM) 算法对潜在的PCOS药物进行排名.
主要方法:
- 用图形理论和拓指数 (班哈蒂,萨格勒布) 来进行化学结构预测.
- 利用定量结构-属性关系 (QSPR) 分析来评估药物的疗效.
- 应用了CRITIC,CoCoSo和MABAC算法用于多标准决策 (MCDM) 药物排名.
主要成果:
- 在拓指数和药物物理化学性质之间发现了强烈的正相关性 (0.8-0.9).
- 通过CoCoSo和MABAC分析,Orlistat被确定为PCOS治疗中最有前途的候选药物.
- 在评估和排名PCOS治疗选择方面,MCDM方法已经证明了其有效性.
结论:
- 拓指数可以显著提高PCOS的药物发现.
- 包括MCDM在内的计算方法为个性化PCOS治疗策略提供了强大的框架.
- 这项研究为开发新药和为PCOS患者量身定制的治疗方案铺平了道路.
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