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

RAPID：合作研究：针对 COVID-19 的计算药物再利用

• 批准号: 2030459
• 负责人: Marinka Zitnik
• 金额: $ 9.99万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030459&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Computational; Drug

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.

鉴于2019冠状病毒病大流行的破坏性，有效的治疗可以挽救重症患者的生命，保护高危感染人群，并减少患者在医院的住院时间。然而，目前还没有针对COVID-19的有效治疗方法。传统的方法需要数年时间来从头开发和测试化合物。机器学习提供了有希望的新方法来重新利用安全且已被批准用于其他疾病的药物。该项目将开发一套机器学习工具，以加快开发安全有效的COVID-19药物。该工具集将迅速确定已批准和实验性药物的安全再利用机会。它将预测治疗是否可能对COVID-19患者产生治疗效果，从而确定安全且足够丰富的药物和药物鸡尾酒，以治疗大量患者。通过将工具交到实践者手中，这个项目中的活动将产生直接的影响。它们将产生准确且可解释的可操作预测。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。在先前基础工作的基础上，在本项目中，主要研究人员将首先构建一个以COVID-19为重点的大型知识图谱，以获取基础和COVID-19特异性生物学知识。图学习方法将用于识别新冠病毒的安全药物和药物鸡尾酒。为了预测含有两种或两种以上药物的鸡尾酒的安全性，该方法将推广到指数级大的高阶药物组合空间。除了药物安全性之外，疗效也是药物开发的一个重要终点。该项目将开发一种新的图形神经网络（GNN）方法，以识别有效的药物再利用机会，甚至适用于尚未有任何药物治疗的疾病，例如COVID-19，因此没有标签和监督信息。该方法将预测哪些药物和药物组合可能对COVID-19产生治疗效果。最后，主要研究人员将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。该项目将为与COVID-19和其他新出现的病原体相关的快速药物再利用提供新的、开放的工具。此外，该项目将为多学科课程开发、培训和咨询以及专业活动提供独特的机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Network medicine framework for identifying drug-repurposing opportunities for COVID-19.

• DOI: 10.1073/pnas.2025581118
• 发表时间: 2021-05-11
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Morselli Gysi D;do Valle Í;Zitnik M;Ameli A;Gan X;Varol O;Ghiassian SD;Patten JJ;Davey RA;Loscalzo J;Barabási AL
• 通讯作者: Barabási AL

Towards a Unified Framework for Fair and Stable Graph Representation Learning

• 发表时间: 2021-02
• 作者: Chirag Agarwal;Himabindu Lakkaraju;M. Zitnik

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Xiang Zhang-;M. Zitnik

MARS: discovering novel cell types across heterogeneous single-cell experiments

• DOI: 10.1038/s41592-020-00979-3
• 发表时间: 2020-10-19
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Brbic, Maria;Zitnik, Marinka;Leskovec, Jure
• 通讯作者: Leskovec, Jure

Subgraph Neural Networks

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Emily Alsentzer;S. G. Finlayson;Michelle M. Li;M. Zitnik