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

Molecular Models02:00

Molecular Models

43.4K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.4K
The Small x Assumption02:20

The Small x Assumption

49.4K
If a reaction has a small equilibrium constant, the equilibrium position favors the reactants. In such reactions, a negligible change in concentration may occur if the initial concentrations of reactants are high and the Kc value is small. In such circumstances, the equilibrium concentration is approximately equal to its initial concentration.  This estimation can be used to simplify the equilibrium calculations by assuming that some equilibrium concentrations are equal to the initial...
49.4K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.7K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.7K
Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

2.0K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
2.0K
Experimental Determination of Chemical Formula02:37

Experimental Determination of Chemical Formula

46.3K
The elemental makeup of a compound defines its chemical identity, and chemical formulas are the most concise way of representing this elemental makeup. When a compound’s formula is unknown, measuring the mass of its constituent elements is often the first step in determining the formula experimentally.
46.3K
Data Validation01:15

Data Validation

566
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:
566

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

Quantum-machine-assisted drug discovery.

npj drug discovery·2026
Same author

Patient-Specific Pessaries After Suboptimal Outcomes With Standard Pessary Use.

Obstetrics and gynecology·2026
Same author

drGT: Interpretable Drug Response Prediction with Attention-Guided Gene Attribution on a Drug-Cell-Gene Heterogeneous Graph.

BMC bioinformatics·2026
Same author

Data-Driven, Mechanistically Guided Prediction of Yield and Chemoselectivity in SuFEx Reactions.

Journal of the American Chemical Society·2026
Same author

Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection.

Foods (Basel, Switzerland)·2026
Same author

Miltefosine attenuates silicosis involving inhibiting the Akt/mTOR signaling pathway.

European journal of pharmacology·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jan 12, 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

1.0K

QCBench:对域特定量化化学的大型语言模型的评估

Jiaqing Xie1, Weida Wang1,2, Ben Gao1,3

  • 1Shanghai Artificial Intelligence Laboratory, 701 Yunjin Road, Xuhui, Shanghai 200232, China.

Journal of chemical information and modeling
|November 3, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 在量化化学计算方面遇到了困难. 一个新的基准,QCBench,随着问题的复杂性增加,显示了显著的性能下降,显示了语言流性和科学准确性之间的差距.

更多相关视频

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K

相关实验视频

Last Updated: Jan 12, 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

1.0K
Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K

科学领域:

  • 量化化学 量化化学是什么
  • 计算化学是一种计算化学.
  • 化学领域的人工智能

背景情况:

  • 定量化学对于现代化学研究至关重要.
  • 大型语言模型 (LLM) 在执行定量化学计算方面的能力尚不清楚.
  • 现有的基准没有充分评估化学LLM的数学推理能力.

研究的目的:

  • 引入QCBench,这是一个新的衡量标准,用于评估定量化学问题上的LLMs.
  • 系统地评估各种化学子领域的LLM的数学推理能力.
  • 为了确定LLMs在科学问题解决中的特定计算弱点和局限性.

主要方法:

  • 开发了QCBench,这是一个基准,包含7个子领域 (分析,生物有机,一般,无机,物理,聚合物和量子化学) 的350个计算化学问题.
  • 将问题分为容易,中等和困难等级,以系统地评估LLM推理.
  • 设计的问题需要明确的数值推理,并防止启发式快捷方式.
  • 在QCBench数据集上评估了24个LLM.

主要成果:

  • 随着定量化学问题的复杂性增加,LLM的表现一直在下降.
  • 在不同难度级别中观察到模型特定限制的显著差异.
  • 该研究强调了LLM的语言流性与他们在科学计算中的准确性之间的差距.

结论:

  • QCBench提供了一个细粒度的诊断工具,用于评估定量化学中的LLM计算弱点.
  • 这些发现强调了需要对领域进行适应性微调和多式联络整合,以提高在科学领域的LLM绩效.
  • 未来的研究可以建立在QCBench的基础上,以推进LLMs在严格的化学计算中的应用.