Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Molecular Models

Molecular Models

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.

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

Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices.

Journal of chemical theory and computation·2026

Same author

Crystal structure and molecular dynamics simulations of rademikibart Fab-IL-4Rα complex reveal biochemical basis for next-generation potent IL-4Rα inhibition in type 2 allergic and inflammatory diseases.

bioRxiv : the preprint server for biology·2026

Same author

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry.

Journal of chemical theory and computation·2026

Same author

Insights on Covalent Attachment of a Re Bipyridyl Catalyst onto <i>p</i>-Si(111) Using an Alkyl Linker for Photoelectrochemical CO<sub>2</sub> Reduction.

Inorganic chemistry·2026

Same author

Electrochemical Nitrate Reduction to Ammonia Driven by Catalytic Monovacancies in Single-Walled Carbon Nanotubes.

The journal of physical chemistry letters·2026

Same author

Mechanism of Tyrosine-Driven Deprotonation in Photosystem II Revealed by Multiscale Simulations.

Journal of the American Chemical Society·2026

Search research articles

相关实验视频

Updated: Jun 8, 2026

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Published on: September 13, 2013

化学空间AL:一种有效的积极学习方法,应用于特定蛋白质的分子生成.

Gregory W Kyro¹, Anton Morgunov¹, Rafael I Brent¹

¹Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States.

Journal of chemical information and modeling

|January 30, 2024

概括

生成型人工智能通过高效生成新型分子来加速药物发现. 这种主动学习方法可以识别针对蛋白质的向药物候选者,如c-Abl激酶和Cas9,甚至可以创建现有的抑制剂.

科学领域:

计算化学是一种计算化学.
医学中的人工智能
药物的发现和开发.

背景情况:

生成型人工智能 (AI) 模型为药物发现中的应用提供了强大的功能.
探索广的化学空间需要有效的方法来识别具有所需性质的分子.
积极学习策略可以优化对新型分子结构的搜索.

研究的目的:

为目标分子生成提供一个计算效率高的主动学习方法.
为了证明这种方法在识别潜在的候选药物的应用性.
为可重复性和实现提供开源软件包.

主要方法:

开发一种用于分子生成的新型积极学习框架.
该方法应用于c-Abl激酶,这是已知的抑制剂的目标.
在CRISPR相关蛋白9 (Cas9) 酶的HNH域上测试该方法,该酶是缺乏已知的抑制剂的标.

主要成果:

积极学习模型成功地产生了类似于已知的c-Abl激酶抑制剂的分子,而没有先前的知识.
该模型完全复制了两种现有的FDA批准的c-Abl激酶的小分子抑制剂.
该方法在生成向Cas9酶的HNH域的分子方面被证明是有效的.

更多相关视频

Optimizing the Genetic Incorporation of Chemical Probes into GPCRs for Photo-crosslinking Mapping and Bioorthogonal Chemistry in Live Mammalian Cells

Optimizing the Genetic Incorporation of Chemical Probes into GPCRs for Photo-crosslinking Mapping and Bioorthogonal Chemistry in Live Mammalian Cells

Published on: April 9, 2018

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems

Published on: June 14, 2021

相关实验视频

Last Updated: Jun 8, 2026

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Published on: September 13, 2013

Optimizing the Genetic Incorporation of Chemical Probes into GPCRs for Photo-crosslinking Mapping and Bioorthogonal Chemistry in Live Mammalian Cells

Optimizing the Genetic Incorporation of Chemical Probes into GPCRs for Photo-crosslinking Mapping and Bioorthogonal Chemistry in Live Mammalian Cells

Published on: April 9, 2018

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems

Published on: June 14, 2021

结论:

提出的积极学习方法在药物发现中对向分子生成具有计算效率和有效性.
这种方法可以识别具有或没有现有抑制剂的蛋白质的新药候选者.
开源的ChemSpaceAL软件包促进了这种人工智能驱动的药物发现技术的实施和可重复性.