CMMS-GCL:与图形对比学习的交叉模式代谢稳定性预测
Bing-Xue Du1,2, Yahui Long3, Xiaoli Li2
1School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Bioinformatics (Oxford, England)
|August 12, 2023
概括
一个新的计算模型,CMMS-GCL,通过整合分子序列和图形数据,准确地预测药物代谢稳定性. 这种可解释的工具有助于有效地选候选药物和优化线.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 代谢稳定性对于药物开发至关重要,影响候选查和优化.
- 对代谢稳定性的实验性评估是昂贵和耗时的.
- 在 silico 预测提供了一个替代方案,但强大的和可解释的方法是有限的.
研究的目的:
- 开发一种用于预测分子代谢稳定的新型计算模型.
- 通过识别关键功能组来提高预测的解释性.
- 为药物发现和率优化提供高效准确的工具.
主要方法:
- 开发了一种跨模态图对比学习模型 (CMMS-GCL).
- 利用深度学习从SMILES序列 (BiGRU编码器) 和分子图形 (图形对比学习编码器) 中提取特征.
- 集成的序列和结构表示使用完全连接的神经网络.
主要成果:
- 在两个基准数据集上,CMMS-GCL的表现优于七种最先进的方法.
- 通过案例研究和统计分析证明了模型的可解释性,确定了关键的功能组.
- 在预测代谢稳定性方面取得了持续的改进.
结论:
- CMMS-GCL是预测药物代谢稳定的有效和可解释的工具.
- 该模型促进了高效的候选药物选和化合物优化.
- 识别了关键的功能组,为药物化学家提供了宝贵的见解.
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