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

Drug Discovery: Overview01:26

Drug Discovery: Overview

10.9K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
10.9K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.6K
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...
1.6K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

4.1K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

14.8K
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...
14.8K
Ligand Binding Sites02:40

Ligand Binding Sites

8.5K
8.5K
Molecular Models02:00

Molecular Models

43.3K
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.
43.3K

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

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.

Journal of chemical information and modeling·2026
Same author

Discovery of 5‑Azaindole Inhibitors of O‑GlcNAcase for the Treatment of Alzheimer's Disease and Related Tauopathies.

ACS medicinal chemistry letters·2026
Same author

3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.

Journal of chemical information and modeling·2026
Same author

Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.

Journal of chemical information and modeling·2025
Same author

C2PO: an ML-powered optimizer of the membrane permeability of cyclic peptides through chemical modification.

Journal of cheminformatics·2025
Same author

MolAgent: Biomolecular Property Estimation in the Agentic Era.

Journal of chemical information and modeling·2025

相关实验视频

Updated: Jan 6, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.3K

强化药物发现的化学语言模型.

Morgan Thomas1,2, Albert Bou1, Jose Carlos Gómez-Tamayo3

  • 1Computational Science Laboratory, Barcelona Biomedical Research Park (PRBB), Universitat Pompeu Fabra, C Dr. Aiguader 88, 08003 Barcelona, Spain.

Journal of chemical information and modeling
|November 16, 2025
PubMed
概括

强化学习 (RL) 增强了药物发现的化学语言模型. 这项研究阐明了RL最佳实践,并介绍了有效分子探索和优化的新方法.

更多相关视频

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.4K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

3.0K

相关实验视频

Last Updated: Jan 6, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.3K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.4K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

3.0K

科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 机器学习用于分子设计.

背景情况:

  • 化学语言模型和强化学习 (RL) 显示出导航广的化学空间的前景.
  • 药物发现的最佳RL算法和最佳实践仍然不清楚.

研究的目的:

  • 调查各种RL组件对化学语言模型性能的影响.
  • 为药物发现应用开发和验证改进的RL策略.
  • 为研究人员将RL应用于化学语言模型提供指导.

主要方法:

  • 基于REINFORCE算法对RL组件 (经验重复,爬山,基线,奖励塑造) 的系统调查.
  • 针对REINFORCE而定制的新型规范化方法的建议.
  • 微调RL超参数以提高有效性和效率.
  • 适用于使用Boltz2作为奖励模型的结合亲和模型.

主要成果:

  • 证明了不同RL组件对学习效率的影响.
  • 引入了一种新的规范化技术,改善了REINFORCE对齐.
  • 展示了RL超参数的微调策略.
  • 通过使用新的奖励模型,在绑定亲和力预测中实现了增强的学习效率.

结论:

  • 这项工作澄清了RL在药物发现中的化学语言模型的最佳实践.
  • 提出的方法和见解可以指导研究人员优化RL用于分子设计.
  • 通过系统的RL组件分析和新技术,提高了学习效率.