Industrializing AI-powered drug discovery: lessons learned from the <i>Patrimony</i> computing platform

As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named ‘<i>Patrimony’</i> was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. Specifically, <i>Patrimony</i> is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&amp;D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.作为一家中型跨国制药企业，我们于四年前启动了一款专为支撑药物发现打造的高通量计算平台。该平台命名为"Patrimony"，其初始设计宗旨为：在充分盘活自有专有数据的同时，整合公开数据源，借助人工智能的技术力量，推动计算精准医学（Computational Precision Medicine）路径的落地。具体而言，Patrimony平台旨在识别新型治疗靶点候选分子。目前该平台已在免疫炎症性疾病领域拥有多项成功应用案例，当前正将应用范围拓展至肿瘤学与神经病学领域。我们在此阐述该工业级计算平台如何对我们的研发工作产生了变革性影响：通过基于模型的理性遴选治疗靶点与药物候选分子，大幅提升研发竞争力，同时实现降本提效。我们不仅汇报了项目取得的成果，还阐述了在推进数据访问与治理流程搭建、硬件与用户界面开发，以及培养科研人员熟练运用预测模型辅助决策过程中所面临的各项挑战。

搜集汇总

背景与挑战

背景概述

该数据集包含两个Excel文件，作为一篇研究文章的补充材料，文章总结了名为Patrimony的AI驱动药物发现计算平台的工业化经验。数据集聚焦于利用人工智能整合多源数据以识别治疗靶点，已在免疫炎症疾病中应用，并正扩展到肿瘤学和神经学领域，体现了AI在药物研发中的转型作用。