基于不足抽样策略和随机森林算法的药物向相互作用预测.
Feng Chen1, Zhigang Zhao2, Zheng Ren2
1School of Advanced Manufacturing Engineering, Hefei University, Hefei, China.
PloS one
|March 6, 2025
概括
这项研究引入了一种改进的计算方法,用于预测药物向相互作用 (DTI). 增强的随机森林模型准确地识别了DTI,克服了药物发现现有方法的局限性.
科学领域:
- 计算化学和生物信息学
- 药物的发现和开发.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物向相互作用 (DTI) 对于药物发现至关重要,但实验性识别是昂贵和耗时的.
- 当前的计算DTI预测模型面临着准确性和高假阳性率的挑战,特别是不平衡的数据集.
- 开发高效的计算方法对于加速识别新型药物标关系至关重要.
研究的目的:
- 开发一种高精度的计算方法,用于预测药物向相互作用 (DTI).
- 解决现有模型的局限性,特别是它们在不平衡数据集上的性能.
- 提高药物发现管道中DTI预测的效率和可靠性.
主要方法:
- 提取药物和标蛋白的综合描述符.
- 集成随机森林 (RF) 模型用于DTI预测的应用.
- 使用随机投影来减少特征尺寸,以简化模型计算.
- 使用NearMiss (NM) 下方采样技术在数据集中平衡样本类别.
主要成果:
- 拟议的方法在所有黄金标准数据集中实现了接收器运行特征曲线 (auROC) 下的高面积得分:92.26% (核受体),98.21% (离子通道),97.65% (GPCRs) 和99.33% (酶).
- 与最先进的方法相比,在预测药物向相互作用方面表现明显优越.
- 减少特征和样本平衡的结合有效地提高了预测准确性.
结论:
- 开发的计算方法显著提高了药物向相互作用预测的准确性.
- 该方法有效地处理不平衡的数据集,这是DTI预测中的一个常见挑战.
- 这项工作提供了一个有前途的工具,可以帮助有效和经济有效地发现新的药物.
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