摘 要
药物研发周期长、成本高且成功率低，传统药物筛选方法存在效率低下等问题。基于人工智能的药物筛选平台构建旨在解决上述问题，通过整合机器学习算法与生物信息学数据，实现高效精准的药物分子筛选。本研究以多种疾病相关靶点为对象，利用深度学习模型对海量化学结构和生物活性数据进行分析挖掘，建立预测模型以评估化合物活性，并开发出一个集成化的人工智能药物筛选平台。该平台不仅包含数据预处理、特征提取等模块，还集成了多种先进的机器学习算法用于模型训练与优化。实验结果表明，相较于传统方法，此平台能够显著提高命中率并缩短筛选时间，在多个测试案例中均表现出优异性能。创新性地将迁移学习应用于小样本量靶点的活性预测，有效解决了数据稀缺问题；同时引入图神经网络处理复杂分子结构信息，提升了模型准确性。本研究为新药研发提供了强有力的技术支持，有望加速药物发现进程，降低研发成本，具有重要的理论意义和应用价值。
关键词：人工智能药物筛选；深度学习模型；迁移学习；图神经网络；生物活性预测
Abstract
Drug development is characterized by long cycles, high costs, and low success rates, with traditional drug screening methods suffering from inefficiencies. The construction of artificial intelligence (AI)-based drug screening platforms aims to address these issues by integrating machine learning algorithms with bioinformatics data to achieve efficient and precise drug molecule screening. This study focuses on multiple disease-related targets, utilizing deep learning models to analyze and mine vast amounts of chemical structures and biological activity data, establishing predictive models to evaluate compound activity, and developing an integrated AI-based drug screening platform. The platform not only encompasses modules for data preprocessing and feature extraction but also integrates various advanced machine learning algorithms for model training and optimization. Experimental results demonstrate that compared to traditional methods, this platform significantly improves hit rates and reduces screening time, exhibiting superior performance across multiple test cases. Innovatively, transfer learning is applied to predict the activity of small-sample targets, effectively addressing data scarcity issues; meanwhile, graph neural networks are introduced to process complex molecular structure information, enhancing model accuracy. This research provides robust technical support for new drug development, potentially accelerating the drug discovery process and reducing development costs, thereby holding significant theoretical and practical value.
Keywords：Artificial Intelligence Drug Screening；Deep Learning Model；Transfer Learning；Graph Neural Network；Bioactivity Prediction
目 录
摘 要 I
Abstract II
引 言 1
第一章 药物筛选平台的需求分析 2
1.1 传统药物筛选的局限性 2
1.2 人工智能在药物筛选中的优势 2
1.3 平台构建的必要性与目标 3
第二章 人工智能技术框架设计 4
2.1 数据获取与预处理方法 4
2.2 模型选择与算法优化 4
2.3 技术实现的关键问题 5
第三章 平台功能模块的构建 7
3.1 化合物数据库建设 7
3.2 预测模型开发与验证 7
3.3 用户交互界面设计 8
第四章 平台的应用与效果评估 10
4.1 实际案例分析 10
4.2 筛选效率对比研究 10
4.3 应用前景展望 11
结 论 12
参考文献 13
致 谢 14
