| 引用本文: | 张家彬,王威,陈川.深度表示学习在环境污染物识别中的应用[J].哈尔滨工业大学学报,2026,58(6):170.DOI:10.11918/202510014 |
| ZHANG Jiabin,WANG Wei,CHEN Chuan.Application of deep representation learning in environmental pollutant identification[J].Journal of Harbin Institute of Technology,2026,58(6):170.DOI:10.11918/202510014 |
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| 摘要: |
| 为应对环境污染物检测成本高昂、依赖动物实验的问题,结构活性关系（QSAR）模型已广泛用于环境中典型污染物（如持久性有机污染物、内分泌干扰物等）的分子活性与生态毒性预测。然而,传统QSAR方法在识别复杂环境样品中的特征污染物时,常受到“活性悬崖”与小样本数据稀缺的问题,导致模型泛化能力不足。针对这些问题,系统梳理了深度学习在环境分子建模中主流的训练范式,包括迁移学习、预训练微调、自监督学习等策略,探讨其在特征提取、模型稳健性及跨任务泛化方面的理论基础与技术实现。文献分析与方法比较显示,这些训练范式在不同类型的自建环境数据集中,相较于传统机器学习方法,在小样本条件下展现出更强的泛化能力、跨任务适应性以及对未知化合物结构特征的识别优势。进一步提出了适用于环境污染物建模与风险评估的分子建模流程,并总结了模型评估体系的关键要点,为构建高泛化性、可解释的环境深度学习模型提供了系统化路径。该研究可为环境领域的模型开发提供结构化的技术指引与模型对比参考框架,促进对不同深度学习训练策略的系统性评估,为行业建立更加统一、可复现的智能建模标准提供支持,从而推动环境污染物筛查与风险评估向数据驱动、智能化方向发展。 |
| 关键词: 环境污染物识别 深度表示学习 分子性质预测 结构活性关系建模 分子生成 |
| DOI：10.11918/202510014 |
| 分类号:X52 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(52400025)；中国博士后科学基金面上项目(2024M754204)；国家污泥安全处置与资源化工程研究中心科学基金(Z2024A001) |
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| Application of deep representation learning in environmental pollutant identification |
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ZHANG Jiabin,WANG Wei,CHEN Chuan
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(State Key Laboratory of Urban-rural Water Resources and Environment (Harbin Institute of Technology), Harbin 150090, China)
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| Abstract: |
| To address the high costs of environmental pollutant detection and the reliance on animal experiments, quantitative structure-activity relationship (QSAR) models have been widely used in predicting the molecular activity and ecotoxicity of typical environmental pollutants (such as persistent organic pollutants and endocrine-disrupting chemicals). However, traditional QSAR methods often suffer from the "activity cliff" and the scarcity of small-sample data when identifying characteristic pollutants in complex environmental samples, resulting in insufficient generalization capability of the models. In view of these issues, this paper systematically reviewed the mainstream training paradigms of deep learning in environmental molecular modeling, including strategies such as transfer learning, pre-training and fine-tuning, and self-supervised learning, and it discussed their theoretical foundations and technical implementations in feature extraction, model robustness, and cross-task generalization. Literature analysis and method comparison show that compared with traditional machine learning methods, these training paradigms demonstrate stronger generalization capability, cross-task adaptability, and identification advantages for structural features of unknown compounds under small-sample conditions across different types of self-constructed environmental datasets. This paper further proposes a molecular modeling workflow suitable for environmental pollutant modeling and risk assessment and summarizes the key points of the model evaluation system, providing a systematic pathway for constructing highly generalizable and interpretable environmental deep learning models. This research can provide structured technical guidelines for model development in the environmental field and a reference framework for model comparison, promote the systematic evaluation of different deep learning training strategies, and offer support for the industry to establish more unified and reproducible intelligent modeling standards, thereby promoting the development of environmental pollutant screening and risk assessment toward data-driven and intelligent directions. |
| Key words: environmental pollutant identification deep representation learning molecular property prediction quantitative structure-activity relationship modeling molecular generation |
