毒素预测器:用于预测分子毒性的计算模型
Mansi Goel1, Arav Amawate2, Angadjeet Singh2
1Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Center of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India.
ToxinPredictor是一种新的机器学习模型,使用结构性质准确预测小分子毒性. 这种计算工具通过有效地识别潜在的毒素来帮助药物发现和环境安全.
科学领域:
- 计算化学的计算化学
- 毒理学 毒理学 毒理学
- 机器学习 机器学习
背景情况:
- 预测分子毒性对于药物发现,环境保护和化学品管理至关重要.
- 传统的实验性毒性测试是资源密集型和耗时的.
- 计算模型为毒性评估提供了一个更快,更具成本效益的替代方案.
研究的目的:
- 开发和验证ToxinPredictor,这是一个用于预测小分子毒性的机器学习模型.
- 确定影响毒性预测的关键分子描述因素.
- 为毒性预测提供一个公开可访问的网络服务器.
主要方法:
- 使用对有毒和无毒分子的精选数据集开发了一个支持向量机 (SVM) 模型.
- 使用了包括Boruta和主要组件分析 (PCA) 在内的特征选择技术.
- 为了模型的可解释性,使用了夏普利添加式扩展 (SHAP) 分析.
主要成果:
- 基于SVM的毒素预测器实现了高性能,接收器操作特征曲线 (AUROC) 下面面积为91.7%,F1得分为84.9%,精度为85.4%.
- 该模型的性能优于现有的计算毒性预测解决方案.
- SHAP分析确定了对毒性预测有贡献的关键分子描述因素.
结论:
- 毒素预测器提供了一个可靠和准确的计算框架来评估分子毒性.
- 该模型提高了药物开发和环境健康评估中的安全性.
- 一个用户友好的网络服务器可用于促进有毒化合物的预测.
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