PLPTP:基于蛋白质语言模型的基于动机的可解释深度学习框架,用于预测毒性
Shun Gao1, Yanna Jia1, Feifei Cui1
1School of Computer Science and Technology, Hainan University, Haikou 570228, China.
Journal of molecular biology
|March 30, 2025
概括
这项研究引入了用于毒性预测的深度学习模型,结合了进化规模建模 (ESM2),双向长期短期记忆 (BiLSTM) 和深度神经网络 (DNN) 以获得准确的药物开发见解.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 生物技术中的机器学习
背景情况:
- 准确的毒性预测对于开发安全的基于的治疗方法至关重要.
- 传统的方法经常与毒性数据的复杂性和不平衡性质作斗争.
研究的目的:
- 开发和验证一种新的深度学习模型,用于增强毒性预测.
- 提高药物开发中识别有毒序的准确性和可靠性.
主要方法:
- 集成进化规模建模 (ESM2) 进行序列上下文,双向长短期记忆 (BiLSTM) 进行依赖提取,以及深度神经网络 (DNN) 进行分类.
- 利用动机分析来提高模型的可解释性和透明度.
- 雇佣焦点损失有效地解决数据集中的阶级不平衡.
主要成果:
- 提出的深度学习模型在多个评估指标上表现出卓越的表现.
- 与传统方法相比,在处理不平衡数据集方面观察到显著的改进.
- 动机分析提供了对模型注意力机制和分类决策的见解.
结论:
- 开发的模型为准确的毒性预测提供了一个强大的工具,有助于设计更安全的药物.
- 这种方法对推动药物开发和生物技术研究具有重大意义.
- 对于更广泛的应用,PLPTP Web 服务器是公开的.
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