I-Corps: Application of deep generative models for simulating biological systems

I-Corps：深度生成模型在模拟生物系统中的应用

• 批准号: 2137197
• 负责人: Roman Lubynsky
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 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: 2021-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137197&HistoricalAwards=false
• 关键词: Corps; Application; deep; generative; models

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI) based platform for simulated biological systems that can predict the true phenotypic outcome of any perturbation prior to wet lab experimentation. The development of the proposed technology addresses the need for a highly predictive, efficient, and cost-effective platform that has potential applications in drug discovery, gene therapy and personalized medicine in the biopharmaceutical industry. Traditionally, the development of drugs, vaccines, and therapies is carried out in biological wet lab settings from preclinical cell and animal models to clinical phase human trials. This research is often expensive, requires years of effort, and can struggle to achieve suitable efficacy. The proposed technology may offer prediction of biochemical changes with high accuracy and maximum efficacy and safety, thereby reducing the burden on payers and stakeholders.This I-Corps project leverages artificial intelligence (AI) and machine learning (ML) through deep generative models (DGMs) to accelerate prediction of phenotypic outcomes in biological systems. Deep neural networks combined with progress in stochastic optimization methods have enabled scalable modeling of complex, high-dimensional data and has become the often preferred artificial intelligence method in computer vision, speech and natural language processing, graph mining, and reinforcement learning. However, there are few examples of its application to biological systems. This project combines the biological processes and DGMs to train the deep neural network in characterizing the phenotype unique to each process. The trained DGMs can then predict the best outcome for any perturbation to these processes in any given environment. The proposed technology is reproducible and scalable, and designed to provide high-content structural, phenotypic, and morphological profiles of the effects of biological and pharmacological substances.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 项目更广泛的影响/商业潜力是为模拟生物系统开发基于人工智能 (AI) 的平台，该平台可以在湿实验室实验之前预测任何扰动的真实表型结果。所提出技术的开发满足了对高度预测、高效且具有成本效益的平台的需求，该平台在生物制药行业的药物发现、基因治疗和个性化医疗方面具有潜在的应用。传统上，药物、疫苗和疗法的开发是在生物湿实验室环境中进行的，从临床前细胞和动物模型到临床阶段人体试验。这项研究通常成本高昂，需要多年的努力，并且很难达到合适的效果。拟议的技术可以提供高精度、最大功效和安全性的生化变化预测，从而减轻付款人和利益相关者的负担。该 I-Corps 项目通过深度生成模型 (DGM) 利用人工智能 (AI) 和机器学习 (ML) ）加速生物系统表型结果的预测。深度神经网络与随机优化方法的进步相结合，使得复杂、高维数据的可扩展建模成为可能，并已成为计算机视觉、语音和自然语言处理、图挖掘和强化学习领域通常首选的人工智能方法。 然而，其应用于生物系统的例子很少。该项目将生物过程和 DGM 结合起来，训练深度神经网络来表征每个过程独特的表型。然后，经过训练的 DGM 可以预测任何给定环境中对这些过程的任何扰动的最佳结果。所提出的技术具有可重复性和可扩展性，旨在提供生物和药理物质作用的高内涵结构、表型和形态特征。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。