I-Corps: Developing predictive computational methods for drug development

I-Corps：开发药物开发的预测计算方法

• 批准号: 2132672
• 负责人: Ernest Petersson
• 金额: $ 5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132672&HistoricalAwards=false
• 关键词: Corps; Developing; predictive; computational; methods

The broader impact/commercial potential of this I-Corps project is to provide the pharmaceutical and biotechnology industries with a software or service that generates accurate predictions for experiments across the life sciences. Drug discovery, biologics design, and other industrial biotechnological efforts often require billions of dollars and decades of research to see projects to completion. Although there has been a major focus over the past two decades to accelerate the drug discovery process using computational tools, the efficacy of existing technologies has typically been limited to a select few experiments which have large amounts of curated data. The proposed technology can provide accurate predictions for experiments across the preclinical discovery space, addressing several steps outside the purview of other contemporary technologies. The flexibility of this technology allows for several time and resource intensive steps to be performed by computer to increase experimental throughput, while allowing researchers to focus on experiments with a high probability for success. This opportunity for improved time and resource management may accelerate the timeline for drugs to enter the clinic and may invigorate therapeutic efforts towards underserved diseases.This I-Corps project will focus on identifying which experiments within the pharmaceutical and biotechnology space are underserved by current computational methods, allowing for these bottlenecks within drug discovery to be addressed. This technology makes use of a novel approach for generating artificial intelligence (AI) models employing information from biophysical simulations. This AI method significantly reduces the amount of data required for generating accurate predictions and has an increased scope of utility compared with traditional AI methods. Proof of concept has been demonstrated through several retrospective studies which demonstrated the technology's ability to provide accurate predictions toward small molecule-protein interactions, protein-protein interactions, therapeutic peptide stabilization, and more. Further exploration of the technology requires insight from potential customers in the pharmaceutical and biotechnology space to understand the specific experiments that this technology needs to address to inform future prospective studies.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.

该I-Corps项目的更广泛的影响/商业潜力是为制药和生物技术行业提供软件或服务，为整个生命科学的实验提供准确的预测。药物发现，生物制剂设计和其他工业生物技术工作通常需要数十亿美元和数十年的研究才能看到项目完成。尽管在过去的二十年中，人们一直在使用计算工具加速药物发现过程，但现有技术的功效通常仅限于具有大量策划数据的精选实验。所提出的技术可以为临床前发现空间的实验提供准确的预测，并在其他当代技术的范围之外解决了几个步骤。该技术的灵活性允许计算机执行几个时间和资源密集型步骤，以增加实验性吞吐量，同时使研究人员能够专注于具有很高成功可能性的实验。这次改善时间和资源管理的机会可能会加速药物进入诊所的时间轴，并可能为服务不足的疾病促进治疗努力。该I-Corps项目将集中于确定药物和生物技术空间中的哪些实验，可以通过当前的计算方法来提供这些瓶颈，从而可以在这些药物中得到这些药物的地址。该技术利用一种新的方法来生成人工智能（AI）模型，该模型采用了来自生物物理模拟的信息。这种AI方法大大减少了产生准确预测所需的数据量，并且与传统的AI方法相比，实用程序的范围增加了。通过几项回顾性研究证明了概念证明，这些研究证明了该技术可以为小分子蛋白相互作用，蛋白质蛋白质相互作用，治疗性肽稳定等提供准确预测的能力。进一步探索该技术需要从药物和生物技术领域的潜在客户的洞察力，以了解该技术需要解决的特定实验，以告知未来的前瞻性研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来支持的。