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

34.2K
VSEPR Theory for Determination of Electron Pair Geometries
34.2K
Classification of Systems-II01:31

Classification of Systems-II

139
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
139
Classification of Systems-I01:26

Classification of Systems-I

179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.3K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.3K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

502
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
502
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

664
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
664

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

Overcoming Shortcut Learning in RNA-Small Molecule Modeling via Bias-Matched Decoys and Structure-Aware Network Design.

Journal of chemical information and modeling·2026
Same author

Hardware-enhanced data security for Internet of Things.

Fundamental research·2026
Same author

Structural insights into single-pass transmembrane receptor GC-A activation by distinct antihypertensive antibodies.

Nature communications·2026
Same author

Prediction accuracy of ligand binding kinetics.

Biophysical journal·2026
Same author

SynFrag: Synthetic Accessibility Predictor Based on Fragment Assembly Generation in Drug Discovery.

Journal of chemical information and modeling·2026
Same author

Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs.

Briefings in bioinformatics·2026
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
查看所有相关文章

相关实验视频

Updated: Jun 21, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.6K

使用后方网络减少分子性质分类中的过度自信错误.

Zhehuan Fan1,2, Jie Yu1,2, Xiang Zhang3

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.

Patterns (New York, N.Y.)
|July 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究通过将Softmax替换为正常化流量来增强药物开发中的分子性质预测. 这提高了不确定性估计,减少了对分布外样本的昂贵过度自信错误预测.

关键词:
在QSAR中使用QSAR.不确定性量化不确定性的量化.人工智能辅助的药物设计值得信赖的AI 值得信赖的AI

更多相关视频

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.6K

相关实验视频

Last Updated: Jun 21, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.6K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.6K

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用

背景情况:

  • 深度学习模型对于预测药物开发中的分子特性至关重要.
  • 传统的基于Softmax的模型表现出不良的不确定性估计,导致对分布外数据进行过度自信的错误预测.
  • 这些限制在药物开发管道中带来了重大风险和成本.

研究的目的:

  • 改进深度学习模型中的不确定性估计,用于分子性质分类.
  • 为了减轻药物开发应用中的过度自信错误预测.
  • 在一个有证据的深度学习框架内使用规范化流程引入一种新的方法.

主要方法:

  • 在深度学习分类模型中,将标准Softmax函数替换为规范化流程.
  • 采用了以证据为基础的深度学习方法,灵感来自后方网络.
  • 评估了对合成数据集,ADMET预测和基于联体的虚拟查的战略.

主要成果:

  • 与传统的软max模型相比,拟议的策略显著减少了过度自信的错误预测.
  • 提高模型可靠性,识别销售外的样本.
  • 在各种分子性质预测任务中表现出更好的性能.

结论:

  • 有证据的深度学习与规范化流提供了一个强大的解决方案,用于不确定性估计在分子性质预测.
  • 开发的框架有效地解决了药物发现当前深度学习模型的关键局限性.
  • 这种方法为更安全,更有效的药物开发提供了有价值的工具.