基于机器学习的抗乳腺癌候选药物的优化模型的研究
Zhou Dong1, Hong Chen1, Yuchen Yang1
1School of Information Engineering, Xi'an Eurasia University, Xi'an, China.
Frontiers in genetics
|April 25, 2025
概括
这项研究引入了一种机器学习模型,通过优化生物活性和ADMET特性来增强抗乳腺癌药物开发,提高药物发现效率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 乳腺癌是全球领先的健康问题,发病率越来越高.
- 目前的治疗方法面临着诸如耐药性和副作用等挑战.
- 需要新的治疗策略和优化的候选药物.
研究的目的:
- 开发一种基于机器学习的优化模型,用于抗乳腺癌药物.
- 增强候选药物的生物活性和ADMET特性.
- 提高药物发现和开发的效率.
主要方法:
- 利用灰色关系和斯皮尔曼相关性分析来确定关键的分子描述符.
- 使用随机森林和SHAP值来选择描述符.
- 开发了使用LightGBM,随机森林和XGBoost的定量结构-活动关系 (QSAR) 模型.
- 应用多模型融合和粒子群集优化 (PSO) 进行多目标优化.
主要成果:
- 确定了91个关键分子描述符,并选择了前20个具有影响力的描述符.
- 使用QSAR模型实现了0.743的R2用于生物活动预测.
- 优化了ADMET特性,对Caco-2 (0.8905) 和CYP3A4 (0.9733) 预测具有高F1分数.
- 在生物活性和药理动力学特性方面均表现出显著的改善.
结论:
- 拟议的机器学习模型为优化抗乳腺癌候选药物提供了一种高效的方法.
- 该模型增强了生物活性和关键的ADMET特性.
- 为未来的药物开发提供了有价值的工具,有可能导致更有效,更安全的乳腺癌治疗方法.
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