Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

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
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

3.0K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
3.0K
Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units01:19

Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units

1.4K
Mathematical principles play a crucial role in pharmacokinetics, providing a framework for understanding and quantifying drug distribution and elimination dynamics in the body. By utilizing mathematical expressions and units, pharmacologists can accurately characterize the behavior of drugs, optimize dosing regimens, and predict therapeutic outcomes.
One significant application of mathematics in pharmacokinetics is the characterization of drug distribution through the volume of distribution...
1.4K
Patch Clamp01:18

Patch Clamp

6.2K
Many fundamental cell functions such as muscle contraction and nerve transmission rely on the electrical signals produced by the movement of positively and negatively charged ions across the cell membrane. One competent method to record current flowing across the whole cell or single ion channel is the patch-clamp technique.
In this method, a glass micropipette containing electrolyte solution is tightly sealed against a small portion of the cell membrane. As a result, a patch of the cell...
6.2K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

648
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
648

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Analysis of monoclonal antibodies against the malaria invasion complex protein RIPR reveals the structural basis for synergistic antibody protection.

Immunity·2026
Same author

ANARCII enables alignment-free antigen receptor numbering using a generalised language model.

Communications biology·2026
Same author

iNOS modulates inflammatory responses in an NO-independent manner through direct interaction with IRG1 in mitochondria.

Nature metabolism·2026
Same author

Rational discovery of therapeutic PAK1 allosteric activators.

Cell·2026
Same author

Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization.

mAbs·2026
Same author

LICHEN enables light-chain immunoglobulin sequence generation conditioned on the heavy chain and experimental needs.

Communications biology·2026

関連する実験動画

Updated: Jan 7, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K

アルゴリズムからシステムへ:計算を創薬に統合する

Anthony R Bradley1,2, Adrian Rossall2, Garry Pairaudeau2

  • 1Department of Chemistry, University of Liverpool, Liverpool, UK.

Expert opinion on drug discovery
|December 25, 2025
PubMed
まとめ

計算創薬は、コストと時間の課題に直面しています。最新のデータインフラストラクチャ、自動化、人工知能(AI)を採用することで、前臨床研究を加速し、効率を向上させることができます。

科学分野:

  • 計算化学
  • 創薬
  • バイオインフォマティクス

背景:

  • 計算の進歩にもかかわらず、前臨床創薬は、増加するタイムラインとコストによって妨げられています。
  • ソフトウェア、データ、自動化は強力なツールを提供しますが、コストと時間の削減の可能性は十分に実現されていません。

研究 の 目的:

  • 創薬能力の進化を議論すること。
  • クラウドネイティブプラットフォーム、能動学習、実験室自動化を含む最新のデータインフラストラクチャを調査すること。
  • 成功例と課題を例示しながら、LLMベースのオーケストレーションやエミュレーションなどの新興技術をカバーすること。

主な方法:

  • 最新のデータインフラストラクチャ(クラウドネイティブプラットフォーム、能動学習)のレビュー。
  • 実験室自動化および新興技術(LLMベースのオーケストレーション、エミュレーション)の調査。
  • 成功例と課題を強調するための実装例の分析。

主要な成果:

  • AIは創薬に新しいパラダイムを提供し、文化的および技術的な変化を必要とします。
  • スケーラブルで堅牢な計算創薬ツールが必要です。
  • 設計サイクルを加速するには、データからの学習効率と自動化に焦点を当てることが重要です。
キーワード:
創薬能動学習人工知能実験室自動化モジュラーシステム

さらに関連する動画

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.1K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.5K

関連する実験動画

Last Updated: Jan 7, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.1K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.5K

結論:

  • より良い経済性を持つモジュラーで相互運用可能な自動化ユニットのより広い採用が重要です。
  • 統計的手法における絶対的な予測精度よりも学習効率を優先することが推奨されます。
  • AIと自動化を統合することで、前臨床創薬を大幅に改善できます。