摘 要：
药物分子设计是生物医学领域的重要环节,把握其规律与特性以及实现药物研发的高效快捷是目前研究的热点。以此为背景，本文在对"生成式AI"的理论基础进行深入剖析的基础上，提出了一种基于多模态预训练模型的药物分子设计创新路径。在研究方法上，本文首先构建了一个含有大量药物分子数据的学习模型，再通过生成式AI的智能算法进行训练和迭代，最后输出符合实验目标的药物分子构造。研究结果表明，多模态预训练模型通过综合理解药物分子的结构、性能和生物活性之间的关系，能有效地实现药物分子的快速设计和优化。相较于传统方法，该创新路径大大降低了药物研发的复杂性和难度，显著提升了药物研发效率，并揭示了新药筛选的新路径，为药物分子设计领域带来了新的研究思路和实验方向。本研究对于未来药物研发的高效并行化和个性化具有项突破性的意义。
关键词：药物分子设计; 多模态预训练模型; 药物研发效率; 新药筛选新路径
Abstract:
Drug molecule design is a crucial aspect in the biomedical field. Understanding its patterns and characteristics, as well as achieving efficient and rapid drug development, are current research hotspots. Against this backdrop, this paper delves into the theoretical foundations of "generative AI" and proposes an innovative path for drug molecule design based on multimodal pre-trained models. In terms of research methodology, this paper first constructs a learning model enriched with a vast amount of drug molecule data. Subsequently, it employs intelligent algorithms of generative AI for training and iterative refinement, ultimately outputting drug molecule structures that align with experimental objectives. The research findings indicate that multimodal pre-trained models can effectively facilitate the rapid design and optimization of drug molecules by comprehensively comprehending the relationships among their structures, properties, and biological activities. Compared to traditional methods, this innovative path significantly reduces the complexity and difficulty of drug development, markedly enhances drug development efficiency, and unveils a new approach for new drug screening. It brings fresh research perspectives and experimental directions to the field of drug molecule design. This study holds groundbreaking significance for the future efficient parallelization and personalization of drug development.
Keywors: Drug molecule design; Multimodal pre-trained models; Drug development efficiency; New approach for new drug screening
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