Predictive Scalability in Developing Large Molecule Therapeutics

开发大分子疗法的预测可扩展性

• 批准号: 2468638
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2468638
• 关键词: Predictive; Scalability; Developing; Large; Molecule

The emerging tools of 'Digital Molecular Technology' promise a step-change transformation of molecule development processes, mainly through new capabilities in data generation, data analysis and knowledge generation. The Lapkin group has developed significant expertise in machine learning (ML) methods for data analysis in formulations and chemical process development. This project will use a combination of mechanistic and non-linear multi-variate statistical models for decision support and knowledge identification in the synthesis of large molecule therapeutics. The key challenge will be the development of a generic workflow that will be flexible in the use of ML tools for the identification of variable interdependencies and sensitivities. The project will start from exploring the possibility of using past experimental data to generate initial grey models, and to test the hypothesis of using past generalised knowledge, then moving to developing decision support tools.

“数字分子技术”的新兴工具主要通过在数据生成、数据分析和知识生成方面的新能力，承诺实现分子开发过程的阶段性变革。Lapkin集团在配方和化学流程开发中的数据分析机器学习(ML)方法方面开发了重要的专业知识。这个项目将使用机械和非线性多变量统计模型的组合，用于大分子疗法合成中的决策支持和知识识别。关键挑战将是开发一种通用工作流程，该工作流程将灵活地使用ML工具来确定各种相互依存关系和敏感性。该项目将从探索使用过去的实验数据来生成初始灰色模型的可能性开始，并测试使用过去的一般性知识的假设，然后转向开发决策支持工具。