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

38.2K
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.
38.2K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K

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

Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.

Molecular systems biology·2026
Same author

Benign-by-design chemistry: Reinventing ligand-based drug design at the edge of AI.

Drug discovery today·2026
Same author

AI agents in drug discovery: applications and case studies.

Drug discovery today·2026
Same author

Counting cells can accurately predict small-molecule bioactivity benchmarks.

Nature communications·2026
Same author

Cohort profile: The Dutch wound monitor cohort and the Swedish Region Halland Integrated Platform (RHIP) wound cohort.

PloS one·2026
Same author

Molecular networking, conformal predictions and revised fingerprint-based models for discovering endocrine disruptors in mixtures.

Analytical and bioanalytical chemistry·2026
Same journal

ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.

Journal of cheminformatics·2026
Same journal

Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.

Journal of cheminformatics·2026
Same journal

MINERVA: a public XAI-powered platform advancing multi-target discovery in Alzheimer's disease.

Journal of cheminformatics·2026
Same journal

Multimodal feature fusion for molecular property classification.

Journal of cheminformatics·2026
Same journal

P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial.

Journal of cheminformatics·2026
Same journal

Computational design of low-volatility lubricants for space using interpretable machine learning.

Journal of cheminformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 2025

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

CPSign:用于化学信息学建模的合规预测.

Staffan Arvidsson McShane1, Ulf Norinder1,2,3, Jonathan Alvarsson1

  • 1Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, 75124, Sweden.

Journal of cheminformatics
|June 29, 2024
PubMed
概括
此摘要是机器生成的。

CPSign 是一种用于化学信息学建模的新开源软件,为可靠的机器学习输出提供符合性预测. 它提供了强大的性能,高效的运行时间和比深度学习模型更低的硬件需求.

更多相关视频

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.1K
Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

5.2K

相关实验视频

Last Updated: Jun 22, 2025

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
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.1K
Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

5.2K

科学领域:

  • 化学信息学 化学信息学
  • 机器学习 机器学习
  • 计算化学的计算化学

背景情况:

  • 符合性预测对机器学习模型进行校准,为制药科学提供关键的有效预测间隔.
  • 现有的方法往往缺乏用于直接化学结构分析和预测的全面工具.

研究的目的:

  • 介绍CPSign,这是一个开源软件,用于化学信息学中的合规预测.
  • 允许用户直接在化学结构上进行数据预处理,建模和预测.
  • 与当代建模方法对比,评估CPSign的性能.

主要方法:

  • 实施了用于分类和回归的感应和传导合规预测.
  • 利用Venn-ABERS的方法来进行概率预测.
  • 支持化学签名和其他描述符,以支持矢量机 (SVM) 为主要建模方法,可扩展到其他模型,如DeepLearning4J.
  • 包括用于结果可视化和发布模型作为REST服务的功能.

主要成果:

  • 与其他方法相比,CPSign表现出强大的预测性能和效率.
  • 在运行时间和硬件要求方面表现优于基于神经网络的模型.
  • 在评估中展示了与最先进的深度学习模型相匹配的性能.
  • 通过在多项研究和生产环境中使用而得到验证.

结论:

  • CPSign为化学信息学建模提供了一个方便,灵活和高效的软件包.
  • 它在符合性预测框架内处理化学输入,描述器计算和SVM建模的能力是一个显著的优势.
  • 该软件为模型构建和评估提供了高水平的抽象性,而不会影响灵活性或性能.