制药公司阿斯利康正在利用人工智能技术加速生物药物发现流程。[1]该公司在美国马萨诸塞州坎德尔广场建设了"未来实验室"设施，将AI与机器人自动化相结合，构建闭环发现系统。[1]阿斯利康生物制药研发工程和肿瘤靶向发现高级副总裁普嘉·萨普拉表示："我们所做的一切，无论是设计、制造、测试还是分析，现在都得到了计算的增强。"[1]
通过应用生成式AI与其他计算工具，药物发现周期有望缩短多达50%。[1]阿斯利康采用多模态专有数据集支撑这一转变，涵盖分子结构、结合测量、安全概况和制造结果等多个维度。[1]该公司的目标还包括实现"从零开始"生成新蛋白质序列的设计，AI需要预测候选药物的安全性、体内行为和可制造性。[1]
Artificial intelligence is fundamentally transforming pharmaceutical research and development. According to McKinsey estimates, generative AI combined with other computational tools could shorten the drug discovery cycle by as much as 50 percent [1]. Pharmaceutical companies are rapidly integrating these technologies into their workflows to accelerate the process of identifying and developing new therapeutic candidates.
AstraZeneca has emerged as a leader in applying AI to biopharmaceutical innovation. The company has established a "Future Laboratory" facility at Kendall Square in Cambridge, Massachusetts, which integrates artificial intelligence with robotic automation to create a closed-loop discovery system [1]. Puja Sapra, Senior Vice President of Biopharmaceutical Research and Development Engineering and Oncology Targeted Discovery at AstraZeneca, described the scope of this computational enhancement, stating: "Everything we do, whether it's design, make, test, or analyze, is now computationally enhanced" [1].
The technological approach employed by AstraZeneca relies on sophisticated data integration and machine learning capabilities. The company utilizes multimodal proprietary datasets that encompass molecular structures, binding measurements, safety profiles, and manufacturing outcomes [1]. A key ambition in this computational strategy is the development of de novo design capabilities, where AI generates entirely novel protein sequences while predicting safety, in vivo behavior, and manufacturability [1]. This convergence of artificial intelligence and laboratory automation represents a significant shift in how potential drug candidates are identified, evaluated, and optimized for clinical development.
