摘要
随着现代药物研发需求的不断增长，新型药物分子的设计与合成路径优化已成为推动医药领域发展的关键环节。本研究旨在通过结合计算化学与实验验证的方法，探索高效、绿色且经济可行的药物分子合成策略。基于量子化学理论和机器学习算法，我们构建了预测分子反应活性及合成可行性的模型，并将其应用于一系列具有潜在治疗价值的化合物设计中。研究选取了若干目标分子，通过逆合成分析与路径筛选，提出了一系列创新性合成路线，显著降低了传统方法中的步骤复杂性和副产物生成量。实验结果表明，所设计的合成路径不仅提高了目标产物的收率，还大幅减少了溶剂消耗与废弃物排放。此外，本研究开发的智能化辅助工具能够快速评估多种合成方案，为药物化学家提供了重要的决策支持。该工作的主要贡献在于将人工智能技术与传统有机合成经验深度融合，开创了一种适用于大规模药物分子开发的高效路径优化框架，为未来药物研发的可持续发展奠定了坚实基础。
关键词：药物分子设计；合成路径优化；量子化学；机器学习；绿色合成
Abstract
With the growing demand for modern drug development, the design of novel drug molecules and the optimization of their synthetic routes have become critical factors in advancing the pharmaceutical field. This study aims to explore efficient, green, and economically viable strategies for drug molecule synthesis by integrating computational chemistry with experimental validation. Based on quantum chemical theory and machine learning algorithms, we developed a model to predict molecular reactivity and synthetic feasibility, which was applied to the design of a series of compounds with potential therapeutic value. Several target molecules were selected, and through retrosynthetic analysis and pathway screening, a set of innovative synthetic routes was proposed, significantly reducing the complexity of steps and by-product formation compared to traditional methods. Experimental results demonstrated that the designed synthetic pathways not only improved the yield of target products but also substantially decreased solvent consumption and waste emissions. Additionally, the intelligent auxiliary tool developed in this study can rapidly evaluate multiple synthetic options, providing crucial decision support for medicinal chemists. The primary contribution of this work lies in the deep integration of artificial intelligence technologies with traditional organic synthesis expertise, establishing an efficient pathway optimization fr amework suitable for large-scale drug molecule development and laying a solid foundation for the sustainable development of future drug research.
Keywords：Drug Molecule Design; Synthetic Pathway Optimization; Quantum Chemistry; Machine Learning; Green Synthesis
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 2
二、药物分子设计理论与方法 2
(一) 分子设计的基本原理 2
(二) 计算机辅助药物设计技术 3
(三) 靶点选择与分子活性预测 3
三、合成路径优化策略研究 4
(一) 合成路径的评估标准 4
(二) 基于绿色化学的合成优化 4
(三) 反应条件对路径效率的影响 5
四、实验验证与案例分析 6
(一) 典型药物分子的设计实例 6
(二) 合成路径优化的实际应用 6
(三) 数据分析与结果讨论 7
结 论 8
参考文献 9
