人工智能赋能新药研发：数智药学的全链条创新实践

Artificial Intelligence–Enabled Drug Discovery and Development: End-to-End Innovation in Digital Pharmaceutics

关键词:  数智药学  人工智能  新药研发  技术转化

Key words:  digital pharmaceutics  artificial intelligence  drug discovery and development  translational implementation.

摘要:

新药研发面临高淘汰率、不同模型体系间的结论传导困难、评价标准不统一等技术瓶颈。为提升研发效率,亟需创新技术与方法。本期“数智药学—人工智能药物研发”研究专栏围绕靶点发现、分析检测、机制解析与实施应用全链条,展示8篇研究成果。通过人工智能算法优化靶点筛选,运用计算机视觉与光谱分析技术强化质量控制,整合多源数据解析药效机制,并从组织管理层面提供量化实施路径。研究成果可以为新药研发的多场景验证与转化提供新思路与技术支撑。

Abstract:

Drug discovery and development face high attrition, limited cross-model translatability, heterogeneous evaluation standards, and long timelines and costs. To improve efficiency, new methods are required. This Special Issue, “Digital Pharmaceutics—Artificial Intelligence for Drug R&D”, presents eight studies spanning the full pipeline from target identification, analytical assessment, and mechanistic elucidation to implementation. The collection demonstrates: AI algorithms for prioritizing targets and candidates; computer vision and spectroscopic analytics to strengthen quality control; multi-source data integration to clarify pharmacological mechanisms; and organization-level pathway analysis to inform implementation. Together, these studies will provide practical directions and technical support for robust, generalizable translation in drug R&D.