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

Hallucinogens and Psychedelics01:27

Hallucinogens and Psychedelics

161
Hallucinogens are psychoactive substances that profoundly alter perceptual experiences, generating unreal visual and sensory images. Often referred to as psychedelic drugs — a term derived from the Greek words "psyche" (mind) and "delos" (revealing) — these substances include marijuana and lysergic acid diethylamide (LSD), among others. These drugs vary in intensity and effects.
Marijuana, derived from the dried leaves and flowers of the hemp plant, contains...
161
CNS Stimulants: Psychedelic Agents01:22

CNS Stimulants: Psychedelic Agents

134
Hallucinogens, also known as psychedelic drugs, are a class of substances known for their ability to alter perception, cognition, and emotions. Despite their profound effects on the mind, these drugs are non-addictive, setting them apart from many other abused substances. The mechanism of action of these drugs lies in their impact on the 5-HT2A receptor in the brain. Upon activation, this receptor couples to Gq-type G proteins, triggering a cascade that releases intracellular calcium. This...
134
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.7K
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...
7.7K
An Overview of Psychoactive Drugs01:28

An Overview of Psychoactive Drugs

199
Psychoactive drugs impact brain function, influencing perception, mood, consciousness, cognition, and behavior. These substances are grouped based on their effects and the mechanisms by which they act.
Stimulants such as cocaine, amphetamines, and nicotine enhance brain activity, leading to increased alertness, attention, and energy. These drugs typically raise heart rate, blood pressure, and body temperature. While they can induce feelings of euphoria, their misuse can result in severe health...
199

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

Efficacy, Structure-Activity Relationship, and Mode of Action Studies of a New Generation of Acridine/Acridone-Based Antimalarials.

ACS infectious diseases·2026
Same author

MegaTrans-machine learning models for drug transporters corresponding to the FDA guidance.

Drug metabolism and disposition: the biological fate of chemicals·2026
Same author

Repurposing Clinical Candidates for Nipah and Hendra Viruses.

ACS infectious diseases·2026
Same author

Enhanced Antiviral Activity of Novel Umifenovir Derivatives against SARS-CoV-2: Insights from an International Collaborative Study.

ACS omega·2026
Same author

Enzyme replacement therapy for CLN1 batten disease that crosses the blood-brain-barrier.

Molecular genetics and metabolism·2026
Same author

Human CYP2C9 Metabolism of Organophosphorus Pesticides and Nerve Agent Surrogates.

Journal of xenobiotics·2026

相关实验视频

Updated: Jun 18, 2025

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

2.5K

使用人工智能预测分子的幻觉潜力.

Fabio Urbina1, Thane Jones1, Joshua S Harris1

  • 1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.

ACS chemical neuroscience
|August 2, 2024
PubMed
概括

研究人员开发了人工智能模型来预测迷幻效应,旨在为像阿片类药物使用障碍这样的心理健康治疗设计更安全的"心理塑原体". 这些模型有助于识别具有治疗潜力的但没有幻觉性质的化合物.

关键词:
符合规范的预测器有幻觉的催眠剂机器学习是机器学习.迷幻类精神药物 迷幻类精神药支持向量的分类支持向量的分类

更多相关视频

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

415
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K

相关实验视频

Last Updated: Jun 18, 2025

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

2.5K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

415
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K

科学领域:

  • 神经科学和药理学 神经科学和药理学
  • 计算化学和化学信息学

背景情况:

  • 被称为"心理塑原剂"的迷幻类药物通过诱导神经可塑性,显示出治疗阿片类药物使用障碍等疾病的前景.
  • 预测幻觉潜在的挑战是由于各种机制,包括G蛋白结合受体 (GPCR) 5HT2A相互作用和复杂的多药学.

研究的目的:

  • 开发人工智能 (AI) 工具,特别是机器学习分类模型,以预测分子的迷幻效应.
  • 设计具有治疗功效但缺乏体内幻觉潜力的新型心理塑料原体.

主要方法:

  • 利用机器学习分类模型,包括支持矢量分类 (SVC) 和随机森林,并进行嵌套的五倍交叉验证.
  • 在体外 (PsychLight) 和体内 (Shulgin的书籍) 人类数据上训练有素的模型,结合ECFP6和静电描述器与合规预测器.
  • 通过预测已知的5HT2A激动剂并使用小鼠头部抽数据评估它们的幻觉潜力来验证模型.

主要成果:

  • 在曲线 (AUC) 下取得的区域为0.74 (PsychLight in vitro) 和0.72 (Shulgin人体数据),用于预测迷幻效应.
  • 模型显示已知5HT2A激动剂的高预测精度,AUC为0.97 (PsychLight) 和0.71 (舒尔金数据).

结论:

  • 人工智能驱动的预测模型在评估心理塑原体候选人的幻觉潜力方面是有效的.
  • 这些工具对于可靠设计新的治疗分子至关重要,这些分子将神经可塑性效应与迷幻体验分开.