人工智能辅助药物分析方法开发的研究进展

Research Progress on the Development of Artificial Intelligence-Assisted Drug Analysis Methods

摘要

人工智能技术不断发展，促使药物分析方法从经验驱动朝着数据驱动转变。传统药物分析方法进行开发时，一般依靠人工试验、经验判断、反复优化。在面对复杂样品、多组分模式、高通量检测需求的情况下，常常会出现周期比较长、变量控制存在困难、结果解释效率不够等问题。机器学习、深度学习、智能优化算法、自动化实验平台被引入进来，这为色谱条件筛选、光谱数据解析、杂质识别、质量评价、过程控制带来了新的技术途径。此文围绕人工智能辅助药物分析方法开发的应用基础、主要进展、现实问题、优化方向展开探讨，希望能为药物质量控制、分析技术创新提供参考。

Abstract

With the continuous development of artificial intelligence technology, drug analysis methods have shifted from being driven by experience to being data-driven. When developing traditional drug analysis methods, they generally rely on manual experiments, experience-based judgments, and repeated optimizations. In the face of complex samples, multi-component patterns, and high-throughput detection requirements, problems such as long development cycles, difficsulties in variable control, and insufficient efficiency in result interpretation often arise. Machine learning, deep learning, intelligent optimization algorithms, and automated experimental platforms have been introduced, which have brought new technical approaches to chromatographic condition screening, spectral data interpretation, impurity identification, quality evaluation, and process control. This article discusses the application basis, main progress, practical problems, and optimization directions of artificial intelligence-assisted drug analysis method development, hoping to provide references for drug quality control and analytical innovation.

关键词

人工智能 / 药物分析 / 方法开发 / 机器学习 / 质量控制

Key words

Artificial Intelligence / drug analysis / method development / machine learning / quality control

参考文献

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