Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Data Validation01:15

Data Validation

164
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
164
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

714
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
714

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Fine-tuning large language models to generate single-atom catalyst synthesis procedures.

Communications chemistry·2026
Same author

From Microcurrents to Macrodynamics: Harnessing Mixed Potentials for Large, Tunable Acceleration of Belousov-Zhabotinsky Oscillations.

Journal of the American Chemical Society·2026
Same author

Strong Dipole-Dipole Interaction Promotes Electrocatalytic Acetylene Semihydrogenation over Symmetric Organo-Electrocatalysts.

Journal of the American Chemical Society·2026
Same author

Amino acid composition drives aggregation during peptide synthesis.

Nature chemistry·2026
Same author

Ligand-Modulated Release of Copper Active Sites Extends Ethylene Production in CO<sub>2</sub> Electroreduction.

Journal of the American Chemical Society·2026
Same author

TANGO: direct optimization of constrained synthesizability for generative molecular design.

Nature computational science·2026

相关实验视频

Updated: Jul 9, 2025

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

594

化学语言模型的快速定制用于分布之外的数据集.

Alessandra Toniato1,2, Alain C Vaucher1,2, Marzena Maria Lehmann3

  • 1IBM Research Europe, Rüschlikon 8803, Switzerland.

Chemistry of materials : a publication of the American Chemical Society
|November 29, 2023
PubMed
概括

用专有数据重新训练语言模型显著提高了化学反应预测和逆合成的准确性. 该方法为企业环境中定制化学语言模型提供了指导方针.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

232
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

相关实验视频

Last Updated: Jul 9, 2025

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

594
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

232
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

科学领域:

  • 人工智能的人工智能
  • 计算化学计算化学
  • 化学信息学 化学信息学

背景情况:

  • 语言模型 (LMs) 对工业应用越来越重要,提供预测洞察力.
  • 在化学工业中,自2016年以来,LM已用于诸如反应结果预测和逆合成等任务.
  • 有限的公共数据集阻碍了LM的性能,需要使用专有数据.

研究的目的:

  • 开发和验证使用专有,非公开的化学数据集重新培训LM的方法.
  • 为了提高反应结果预测和单步逆合成模型的准确性.
  • 建立在企业环境中定制化学LM的指南.

主要方法:

  • 在专有,非公开的数据集上重新训练语言模型.
  • 应用一种结合专利和专有数据的多领域学习公式.
  • 验证反应结果预测和单阶段逆合成任务的方法.

主要成果:

  • 通过使用专有数据进行再培训,实现了模型精度的显著提高.
  • 在多领域学习方法中将专利和专有数据结合起来,可以获得相当大的准确性收益.
  • 开发的方法论证明了提高化学LM性能的成功方法.

结论:

  • 专有数据集对于提高化学语言模型的性能至关重要.
  • 多领域学习为利用多种数据源提供了一个强大的策略.
  • 该研究为工业中化学LMM的有效定制提供了实际指导方针.