ERRα-Predictor:一个集合模型框架,用于使用人工智能预测ERRα绑定器,对手和对手
Le Xiong1, Jiahao Xu1, Hongbo Yu1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
我们开发了ERRα-Predictor模型,通过使用机器学习和图形神经网络来预测雌激素相关受体α (ERRα) 结合剂,对抗剂和激动剂来识别潜在的癌症和代谢疾病治疗方法.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 与雌激素相关的受体α (ERRα) 是治疗癌症和代谢疾病的关键点.
- 开发准确的ERRα配体 (结合剂,抗剂,激动剂) 的预测模型对于药物发现至关重要.
研究的目的:
- 创建和验证ERRα小分子配体的预测模型.
- 为分析ERRα连接体结构和特性提供一个框架.
主要方法:
- 来自多个数据库的公开可用的ERRα配体数据.
- 使用机器学习和图形神经网络开发了基线模型.
- 构建集成ERRα-Predictor模型,集成SMILES和图形拓数据.
主要成果:
- 在测试和外部验证集上,ERRα-Predictor模型实现了高性能 (MCC 0.633,0.560,0.545).
- 解释性分析 (SHAP,GNNExplainer) 提供了对模型预测的见解.
- 匹配分子对分析 (MMPA) 确定了关键的结构修改,以优化连接体.
结论:
- 该ERRα-Predictor框架提供了一种可靠的方法来预测和分析ERRα小分子连接体.
- 该研究提供了有价值的数据和工具,以加速ERRα向疗法的开发.
- 为ERRα-Predictor提供开源代码,促进进一步的研究和应用.
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