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相关概念视频

Free Energy01:21

Free Energy

47.7K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
47.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jun 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

使用增强数据缩小机器学习评分函数和自由能量扰动之间的差距.

Ísak Valsson1, Matthew T Warren2, Charlotte M Deane1

  • 1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.

Communications chemistry
|February 8, 2025
PubMed
概括

我们开发了一个新的机器学习模型,AEV-PLIG,用于准确的结合亲和力预测. 这种模型表现出具有竞争力的性能,并且比传统的基于物理的方法快得多,有助于药物发现.

相关实验视频

Last Updated: Jun 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习是机器学习.

背景情况:

  • 机器学习模型承诺快速结合的亲和力预测,但缺乏对药物发现任务 (如优化) 的强有力的评估.
  • 目前的模型在排名同源联体序列方面扎,这限制了它们在现实世界中的应用.

研究的目的:

  • 引入一种基于注意力的新型图形神经网络,AEV-PLIG,以改进结合亲和力预测.
  • 为评估机器学习模型开发一种新的,现实的分销外测试集 (OOD测试).
  • 严格评估机器学习模型的性能与基于物理的方法.

主要方法:

  • 开发了基于注意力的图形神经网络模型AEV-PLIG.
  • 引入了用于分销之外评估的OOD测试套件.
  • 在OOD测试上使用基准AEV-PLIG,CASF-2016和自由能量扰动 (FEP) 基准.

主要成果:

  • 与基于物理的方法相比,AEV-PLIG显示出具有竞争力的性能.
  • 增强数据显著改善了预测相关性和FEP基准的排名 (PCC:0.41到0.59,肯德尔的t:0.26到0.42).
  • AEV-PLIG的计算速度大约是FEP计算的40万倍.

结论:

  • AEV-PLIG提供了一种强大而高效的机器学习方法,用于绑定亲和力预测.
  • 开发的策略与FEP计算弥合了绩效差距,提高了药物发现效率.
  • AEV-PLIG为药物发现应用程序的机器学习模型提供了现实的评估.