循环神经网络预测药物开发的未来类聚合
Prageeth R Wijewardhane1, Katelyn Smith2, Jonathan Fine1,2
1Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States.
Molecular pharmaceutics
|October 15, 2025
概括
人工智能 (AI) 模型使用thioflavin T (ThioT) 试验预测聚合. 循环神经网络准确地预测未来的ThioT曲线,优化制药开发并减少资源需求.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 活性药物成分 (API) 的物理稳定性对于药物开发至关重要.
- 溶液条件显著影响治疗性的稳定性.
- 提奥夫拉T (ThioT) 测定测量聚,但大规模研究是资源密集的.
研究的目的:
- 开发人工智能 (AI) 方法来预测聚和ThioT曲线.
- 为了能够快速和经济有效地预测的物理稳定性.
- 为了减少在药物开发中需要广泛的,资源繁重的稳定性测试的需要.
主要方法:
- 拟定聚合预测作为一种自然语言处理"语言翻译"问题.
- 开发了一种基于长期短期记忆 (LSTM) 的循环神经网络 (RNN) 模型.
- 使用初始和1个月的ThioT测试数据来预测未来 (6个月和12个月) 的ThioT曲线.
主要成果:
- 对于预测6个月的ThioT曲线,LSTM模型实现了2.04的绝对平均平均误差 (MAE).
- LSTM模型的预测经过实验验证.
- 无论是LSTM和多层感知器 (MLP) 模型,在12个月的时间点上都显示了可比的MAE,但数据有限.
结论:
- LSTM模型可以使用短期稳定性数据 (初始和1个月) 准确预测未来的ThioT曲线.
- 循环神经网络模型为制药行业提供了有价值的工具.
- 这些人工智能模型可以加速对API物理稳定性制定格局的探索.
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