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

Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...

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

Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using Publicly Available Docking Programs.

Molecular informatics·2025
Same author

Lessons learnt from machine learning in early stages of drug discovery.

Expert opinion on drug discovery·2024
Same author

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking.

Journal of chemical information and modeling·2023
Same author

How good are AlphaFold models for docking-based virtual screening?

iScience·2023
Same author

Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point.

ACS omega·2023
Same author

Solvent effects on the NMR shieldings of stacked DNA base pairs.

Physical chemistry chemical physics : PCCP·2022

相关实验视频

Updated: May 16, 2026

A Robust Method for the Large-Scale Production of Spheroids for High-Content Screening and Analysis Applications
06:40

A Robust Method for the Large-Scale Production of Spheroids for High-Content Screening and Analysis Applications

Published on: December 28, 2021

3.1K

评估机器学习方法的稳定性和可扩展性,以加速超大高通量对接活动.

Juan I Di Filippo1,2, Santiago Rómoli1,2, Claudio N Cavasotto1,2,3,4

  • 1Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires 1629, Argentina.

ACS omega
|April 28, 2025
PubMed
概括

机器学习 (ML) 协议通过在大型化学图书馆中有效地识别最高得分的分子,显著加速药物发现. 这项研究验证了ML的有效性.

更多相关视频

Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
08:15

Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease

Published on: May 10, 2024

468
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

2.2K

相关实验视频

Last Updated: May 16, 2026

A Robust Method for the Large-Scale Production of Spheroids for High-Content Screening and Analysis Applications
06:40

A Robust Method for the Large-Scale Production of Spheroids for High-Content Screening and Analysis Applications

Published on: December 28, 2021

3.1K
Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
08:15

Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease

Published on: May 10, 2024

468
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

2.2K

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 基于结构的虚拟查对于计算药物发现至关重要.
  • 机器学习 (ML) 协议加速了大型化学库的高通量选.
  • 以前的ML验证研究使用了有限的目标和小分子库.

研究的目的:

  • 为加速虚拟查扩展ML协议的验证.
  • 在大型 (∼100M) 和多样化的 (10蛋白标) 化学库上评估ML性能.
  • 为了证明ML在从超大数据集中获取最高分数分子的效率.

主要方法:

  • 利用了两个标准的公开可用的100M分子库.
  • 在10个蛋白质标中使用了10M分子的综合基准集.
  • 使用了PLANTS和AutoDock的Vina对接程序来进行分子对接分数.
  • 验证的ML协议用于检索虚拟命中和评估性能指标.

主要成果:

  • ML协议从10M分子组中分别检索到>60%和>70%的顶级10k和1k分子.
  • 平均而言,达到了>97%的对接评估减少.
  • 证明增加培训集大小在较大的图书馆中比例提高了ML性能.
  • 在各种目标和大型数据集中确认了ML协议的强大性能.

结论:

  • ML方法对于从数亿到数十亿化合物的化学库中获取最高分数的分子是非常有效的.
  • 机器学习显著降低了虚拟选所需的计算成本和时间.
  • 机器学习模型的作用对于探索巨大的化学空间而言至关重要,在这些空间中,粗暴的强力对接是不可行的.