摘要
随着人工智能技术的快速发展，其在药物研发领域的应用潜力日益凸显，传统药物研发周期长、成本高且成功率低的问题亟需创新性解决方案。本研究旨在开发一种基于人工智能的药物研发辅助工具，以提升药物发现效率并降低研发成本。研究采用深度学习与图神经网络相结合的方法，构建了能够预测化合物活性、优化分子结构以及筛选潜在候选药物的智能模型。通过对大规模公开数据库的训练和验证，该工具展现出优异的预测精度和泛化能力，特别是在虚拟筛选和先导化合物优化环节表现突出。此外，本研究创新性地引入了强化学习算法，实现了自动化分子设计与优化流程，显著提高了药物研发的智能化水平。实验结果表明，该工具能够在较短时间内识别出具有高活性的候选化合物，并有效减少实验验证的工作量。总体而言，本研究为药物研发领域提供了高效、可靠的智能化解决方案，其创新性的技术框架和出色性能有望推动新药研发进入更加精准和高效的阶段。
关键词：人工智能药物研发；深度学习；图神经网络；强化学习；分子优化
Abstract
With the rapid development of artificial intelligence technologies, their application potential in drug discovery is becoming increasingly prominent, as traditional drug development faces challenges such as long cycles, high costs, and low success rates, necessitating innovative solutions. This study aims to develop an artificial intelligence-based tool for assisting in drug discovery to enhance efficiency and reduce costs. By integrating deep learning with graph neural networks, a smart model was constructed to predict compound activity, optimize molecular structures, and screen potential drug candidates. Trained and validated on large-scale public databases, the tool demonstrated excellent predictive accuracy and generalization capabilities, particularly excelling in virtual screening and lead compound optimization. Additionally, this study innovatively incorporated reinforcement learning algorithms to achieve automated molecular design and optimization processes, significantly improving the level of intelligence in drug development. Experimental results indicate that the tool can identify highly active candidate compounds within a short timefr ame while effectively reducing the workload of experimental validation. Overall, this research provides an efficient and reliable intelligent solution for drug discovery, and its innovative technical fr amework and superior performance are expected to advance new drug development into a more precise and efficient era.
Keywords：Artificial Intelligence Drug Discovery; Deep Learning; Graph Neural Network; Reinforcement Learning; Molecular Optimization
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 1
二、人工智能技术在药物研发中的应用基础 2
(一) 药物研发流程与痛点分析 2
(二) 人工智能技术的核心能力 3
(三) AI在药物研发中的典型应用场景 3
三、基于人工智能的药物研发辅助工具设计 4
(一) 工具开发的需求分析 4
(二) 关键技术框架构建 4
(三) 数据处理与模型训练策略 5
四、辅助工具的功能实现与性能评估 5
(一) 功能模块的设计与实现 5
(二) 性能测试与结果分析 6
(三) 实际案例验证与优化建议 7
结 论 8
参考文献 9
