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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

44.5K
VSEPR Theory for Determination of Electron Pair Geometries
44.5K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

226
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
226

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

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
Same author

Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.

Journal of chemical theory and computation·2026
Same author

Wavefront estimation through structured detection in laser scanning microscopy.

Biomedical optics express·2026
Same author

ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction.

Journal of chemical information and modeling·2025
Same author

SARS-CoV-2 Entry Can Be Mimicked in <i>C. elegans</i> Expressing Human ACE2: A New Tool for Pharmacological Studies.

Viruses·2025
Same author

GENEOnet: a breakthrough in protein binding pocket detection using group equivariant non-expansive operators.

Scientific reports·2025
Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026
Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026
Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

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

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K

来自机器学习的力场参数的凝结预测了高通量虚拟选应用程序的分布.

Domenico Bonanni1,2, Yuedong Zhang3, Davide Gadioli3

  • 1Department of Physical and Chemical Sciences, University of L'Aquila, 67100 Coppito, Italy.

Journal of chemical information and modeling
|November 22, 2025
PubMed
概括
此摘要是机器生成的。

一种新的机器学习方法缩小了力场参数,在生物分子模拟中显著提高了30倍的计算效率. 这种方法保持了高精度,使复杂的分子建模更容易获得.

更多相关视频

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

583
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K

相关实验视频

Last Updated: Jan 10, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K
Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

583
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K

科学领域:

  • 计算化学计算化学
  • 分子动力学分子动力学
  • 机器学习 机器学习

背景情况:

  • 传统的可转移生物分子力场很难与新数据更新.
  • 机器学习力场 (MLFF) 提供了准确性和适应性,但对于高通量虚拟选 (HTVS) 来说,它们在计算上昂贵.

研究的目的:

  • 为MLFF参数开发一种新的凝结方法,以提高计算效率.
  • 与现有方法相比,评估缩MLFF的准确性和性能.

主要方法:

  • 利用机器学习算法来预测和缩小力场参数.
  • 开发了一种统计方法,在缩小的参数中表示化学变异性.
  • 在OpenFF行业基准数据集上评估了缩的MLFF.

主要成果:

  • 在计算效率方面实现了30倍的改进.
  • 与分子特异性参数相比,只观察到精度 (RMSD和TFD) 略有下降.
  • 缩MLFF显示了与已建立的可转移力场相比具有竞争力的性能.

结论:

  • 拟议的凝结方法显著提高了MLFF的计算效率,而不会大幅降低准确度.
  • 这种方法为将MLFF集成到HTVS和大型生物分子模拟中提供了可行的解决方案.