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

Drug Discovery: Overview

Drug Discovery: Overview

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...

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

Peptide Thioester and Triazole Derivatives Through On-Resin Dual-Modification of Peptide Thiosulfonates.

Angewandte Chemie (International ed. in English)·2026

Same author

Computer-guided enzyme engineering of PET hydrolase mutants towards improved PET affinity.

BMC biotechnology·2026

Same author

A Polypharmacology-Driven Approach to Alzheimer's Disease and Tauopathies: Rational Design, Synthesis and Characterization of Amino-Pyrazole-Based Multikinase (GSK-3β/FYN-α/DYRK1A) Inhibitors.

Journal of medicinal chemistry·2026

Same author

Critical Assessment of a Structure-Based Pipeline for Targeting the Long Noncoding RNA MALAT1.

Journal of chemical information and modeling·2026

Same author

Targeting RAD51-BRCA2 Interaction to Enhance Synthetic Lethality with Olaparib in Pancreatic Cancer: Development of a Novel Phenyl Furan-Quinoline-Carboxylic Acid Series.

ACS medicinal chemistry letters·2026

Same author

A Metabolites' Interplay Can Modulate DNA Repair by Homologous Recombination.

International journal of molecular sciences·2026

Search research articles

相关实验视频

Updated: Jan 16, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

解码不一致的生物数据:在药物发现中实现增强人工智能预测能力的关键步骤

Mira A M Behnam¹, Andrea Cavalli², Diana Lousa³

¹Medicinal Chemistry, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, Heidelberg 69120, Germany.

ACS pharmacology & translational science

|January 15, 2026

概括

来自不同来源的生物活性数据的结合在机器学习 (ML) 模型中引入噪音. 解决测试协议变异对于准确的计算药物发现和蛋白质-连接体相互作用研究至关重要.

关键词:

人工智能的人工智能是人工智能.生物活性数据生物活性数据形状塑性形状塑性形状塑性机器学习是机器学习.蛋白质酶蛋白质酶是一种蛋白质.测试条件测试条件

更多相关视频

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

相关实验视频

Last Updated: Jan 16, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物化学生物化学

背景情况:

来自不同来源的生物活性数据的结合为机器学习 (ML) 模型引入了噪音.
测试协议的变化 (缓冲组合,实验设置) 显著影响数据可靠性.
酶和病毒表面蛋白质等蛋白质点表现出受外部因素影响的结构变化.

研究的目的:

突出测试方案变化的对生物活性数据的影响.
讨论在计算药物发现中减轻噪音的策略.
探索深度学习 (DL) 和大型语言模型 (LLM) 在应对这些挑战方面的潜力.

主要方法:

分析测试方案的变化及其对蛋白质标的影响.
对处理酶抑制剂/结合剂数据的策略的审查.
讨论深度学习 (DL) 模型的实用性.
探索当前计算蛋白质-连接体相互作用研究的局限性.
专家采访大语言模型 (LLM) 和代理人工智能.

主要成果:

测试协议的差异是生物活性数据集中噪声的主要来源.
酶和病毒蛋白的形态变化使数据集成复杂化.
深度学习 (DL) 模型看起来有前途,但也有局限性.
LLM和代理AI为药物发现提供了潜在的进步.

结论:

标准化测试协议对于可靠的ML模型培训在药物发现中至关重要.
需要先进的计算方法,包括DL和LLM,以克服数据异质性.
需要进一步的研究才能充分利用人工智能来预测蛋白质-连接体相互作用.