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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

716
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
716
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53

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

Finding Balance: Multiobjective Optimization in Molecular Generative Modeling.

Journal of chemical information and modeling·2026
Same author

A Return to the Logic-First Spirit of Corey's Retrosynthetic Analysis, Now Implemented in Modern Data-Driven CASP.

ACS central science·2026
Same author

FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.

Nature computational science·2026
Same author

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.

Journal of chemical theory and computation·2026
Same author

Synthesizability <i>via</i> reward engineering: expanding generative molecular design into synthetic space.

Chemical science·2026
Same author

TANGO: direct optimization of constrained synthesizability for generative molecular design.

Nature computational science·2026
Same journal

Enhanced and selective oxygen reduction by iron porphyrin with a biguanide residue in the second coordination sphere.

Chemical science·2026
Same journal

Excited-state orbital angular momentum enables all-optical molecular spin coherence.

Chemical science·2026
Same journal

Polyvinyl-based hole-transporting materials processed with non-destructive and green solvents for tin-lead perovskite solar cells and all-perovskite tandems.

Chemical science·2026
Same journal

Pd-catalyzed regio- and enantioselective allylation of cyclic allylboronates.

Chemical science·2026
Same journal

Covalent polyoxometalate-polyimide hybridization: multi-scale molecular engineering toward high-performance sodium-ion battery anodes.

Chemical science·2026
Same journal

Catalytic visible light-driven alkane dehydrogenation by a di-uranyl germanotungstate.

Chemical science·2026
查看所有相关文章

相关实验视频

Updated: Jun 30, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

样本高效的强化学习与分子设计的积极学习.

Michael Dodds1, Jeff Guo1, Thomas Löhr1

  • 1Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden jonpaul.janet@astrazeneca.com.

Chemical science
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的强化学习与主动学习 (RL-AL) 系统,以加速药物发现. RL-AL显著提高了样本效率,加速了在复杂化学空间中寻找新药候选者的速度.

更多相关视频

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.0K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

9.8K

相关实验视频

Last Updated: Jun 30, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.0K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

9.8K

科学领域:

  • 计算化学和药物发现
  • 科学研究中的人工智能
  • 机器学习用于分子设计

背景情况:

  • 强化学习 (RL) 对高维问题是有效的,但在复杂的科学环境中,它在样本效率方面存在困难.
  • 药物发现需要有效地探索广的化学空间和多参数优化 (MPO).
  • 目前的in silico方法,如虚拟选和de novo生成,需要提高复杂模型的样本效率.

研究的目的:

  • 为了提高分子设计的强化学习 (RL) 的样本效率.
  • 开发和评估一种与RL (RL-AL) 集成的新型主动学习 (AL) 系统,用于多参数优化 (MPO).
  • 解决结合RL和AL的挑战,以实现有效的分子发现.

主要方法:

  • 积极学习 (AL) 系统与强化学习 (RL) 模型的整合,称为RL-AL.
  • 开发一种新的AL方法,专门用于解决分子设计中的多参数优化 (MPO) 问题.
  • 与基线RL进行比较分析,使用基于连接体和结构的Oracle函数.

主要成果:

  • 对于固定Oracle预算而言,RL-AL显示了生成的访问量增加了5-66倍,计算时间减少了4-64倍.
  • 通过RL-AL发现的化合物显示了多参数评分目标的显著丰富,这表明在识别高得分分子方面具有卓越的性能.
  • RL-AL方法保持了输出多样性,同时提高了高得分化合物治愈的有效性.

结论:

  • 与基线RL相比,RL-AL系统大大加快了寻找新型分子解决方案的速度.
  • 这种方法提高了昂贵的计算预言函数的可行性,以前由高成本限制.
  • RL-AL方法广泛适用于任何需要提高样本效率和优化的RL领域.