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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...

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Updated: Jan 18, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

通过机器学习启用数组选多层进化组合图书馆来打击假冒药物.

Huihai Li¹, Hao Chen², Weiwei Ni¹

¹State Key Laboratory of Natural Medicines, National R&D Center for Chinese Herbal Medicine Processing, Jiangsu Key Laboratory of Drug Design and Optimization, College of Engineering, China Pharmaceutical University, Nanjing 211109, China.

ACS applied materials & interfaces

|September 12, 2025

概括

此摘要是机器生成的。

一种新的机器学习策略快速识别出最佳的传感器阵列,用于检测假冒的非类固醇抗炎药物 (NSAIDs). 这种方法在几分钟内确保100%的准确性来区分真实的NSAID和假的NSAID.

关键词:

组合图书馆是一个组合图书馆.识别假冒药物的身份.机器学习是机器学习.非类固醇抗炎药物是指非类固醇抗炎药物.传感器阵列是一系列的传感器阵列.

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相关实验视频

Last Updated: Jan 18, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

A Fluorescence-based Lymphocyte Assay Suitable for High-throughput Screening of Small Molecules

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科学领域:

分析化学分析化学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

假冒药物对全球健康构成重大风险,影响患者的发病率和死亡率.
传感器阵列为分辨药物分子提供了潜力,但在快速生成图书馆方面面临着挑战.
开发有效的方法来确定最佳的传感器组合对于药物的真实性验证至关重要.

研究的目的:

开发一种以机器学习为指导的战略,快速生成最佳传感器阵列.
识别一组最小的传感元件,用于检测假冒的非类固醇抗炎药物 (NSAIDs).
建立在药物真实性验证中广泛应用的基础.

主要方法:

采用了由机器学习指导的三层选策略.
一个由100个候选传感元件组成的组合设计的库被合成和选.
一个修剪的5元素阵列被构建并验证了NSAID歧视.

主要成果:

5元素阵列在区分9种NSAID及其类型时达到100%的准确性.
成功证明了两个关键的NSAIDs的定量和多重差异化.
该阵列在5分钟内准确地从两个假冒版本中识别出了五种商用非处方 (OTC) NSAID,准确度为100%.

结论:

这种以机器学习为指导的策略能够快速构建最佳的组合传感库.
开发的传感器阵列为伪造的NSAID检测提供了一个高度准确和高效的方法.
这种方法为多功能药物真实性验证系统奠定了基础.