RAPID: Collaborative Research: Computational Drug Repurposing for COVID-19

由于新冠肺炎疫情具有破坏性，有效的治疗可以挽救重症患者的生命，保护感染高危人群，并减少患者在医院病床上的时间。然而，目前还没有有效的治疗新冠肺炎的方法。传统的方法需要数年时间才能从零开始开发和测试化合物。机器学习为改变药物的用途提供了很有前途的新方法，这些药物是安全的，并且已经被批准用于其他疾病。该项目将开发一个机器学习工具集，以加快为新冠肺炎开发安全有效的药物。该工具包将迅速确定已批准和试验药物的安全再利用机会。它将预测治疗是否对新冠肺炎患者有疗效，从而能够识别出足够安全和充足的药物和药物鸡尾酒，足以治疗相当数量的患者。通过将工具交到实践者手中，这个项目中的活动将产生立竿见影的影响。它们将产生准确和可解释的可操作的预测。最近，主要研究人员开发了一系列机器学习工具来识别药物再利用的机会。在先前基础性工作的基础上，在这个项目中，主要研究人员将首先构建一个以新冠肺炎为重点的大型知识图谱，其中将捕捉基础和新冠肺炎特有的生物学知识。图学习方法将被用于为新冠肺炎识别安全的药物和药物鸡尾酒。为了预测含有两种或两种以上药物的鸡尾酒的安全性，这些方法将推广到指数级的高阶药物组合空间。除了药物安全性外，疗效也是药物开发的关键终点。该项目将开发一种新的图形神经网络方法来识别有效的药物再利用机会，即使是对于新冠肺炎等尚未有任何药物治疗从而没有标签的监督信息的疾病也是如此。该方法将预测哪些药物和药物组合可能对新冠肺炎有治疗效果。最后，首席调查人员将把开发的工具集成到一个完整的、可解释的框架中，该框架将生成预测、提供解释，并将人类反馈纳入机器学习循环。该项目将为快速重新调整药物用途提供新的、开放的工具，这将与新冠肺炎和其他新兴病原体相关。此外，该项目将为多学科课程开发、培训和建议以及专业活动提供独特的机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With the disruptive nature of the COVID-19 pandemic, effective treatments could save the lives of severely ill patients, protect individuals with a high risk of infection, and reduce the time patients spend in hospital beds. However, there are currently no effective treatments for COVID-19. Traditional methodologies take years to develop and test compounds from scratch. Machine learning provides promising new approaches to repurpose drugs that are safe and already approved for other diseases. This project will develop a machine learning toolset to expedite the development of safe and effective medicines for COVID-19. The toolset will rapidly identify safe repurposing opportunities for approved and experimental drugs. It will predict whether treatments may have therapeutic effects in COVID-19 patients, allowing the identification of drugs and drug cocktails that are safe and plentiful enough to treat a substantial number of patients. By putting tools in the hand of practitioners, the activities in this project will have an immediate impact. They will result in actionable predictions that are accurate and interpretable. Recently, the principal investigators have developed a series of machine learning tools to identify drug repurposing opportunities. Building on foundational previous work, in this project, the principal investigators will first build a large COVID-19 focused knowledge graph that will capture fundamental and COVID-19-specific biological knowledge. The graph learning methods will be adapted to identify safe drugs and drug cocktails for COVID-19. To predict the safety of cocktails with two or more drugs, the methods will generalize to an exponentially large space of high-order drug combinations. In addition to drug safety, efficacy is a crucial endpoint for drug development. The project will develop a novel graph neural network (GNN) method to identify efficacious drug repurposing opportunities, even for diseases, such as COVID-19, that do not yet have any drug treatments and thereby, no label, supervised information. The method will predict what drugs and drug combinations may have a therapeutic effect on COVID-19. Finally, the principal investigators will integrate the developed tools into a complete, explainable framework that will generate predictions, provide explanations, and incorporate human feedback into the machine learning loop. This project will provide new, open tools for rapid drug repurposing that will be relevant for COVID-19 and other emerging pathogens. Additionally, the project will provide unique opportunities for multi-disciplinary curriculum development, training and advising, and professional activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.