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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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  1. 首页
  2. 循环神经网络预测药物开发的未来类聚合.
  1. 首页
  2. 循环神经网络预测药物开发的未来类聚合.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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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

在PubMed 上查看摘要

概括
此摘要是机器生成的。

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.

关键词:
深度学习是一种深度学习.药物开发是药物的发展.长时间的短期记忆 (LSTM)质聚合的聚.物理稳定性预测预测序列对序列建模的模型治疗性类的治疗性类.提奥夫拉T (ThioT) 试验法

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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.