SBIR Phase II: Accelerating R&D through Streamlined Machine Learning Algorithms for Small Data Applications in Advanced Manufacturing

SBIR 第二阶段：加速 R

• 批准号: 2325045
• 负责人: Daniela Blanco
• 金额: $ 100万
• 依托单位: SUNTHETICS, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325045&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Accelerating; R&amp

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project will improve and accelerate the development of new chemicals, processes, and formulations in the pharmaceutical industry. By harnessing the power of machine learning (ML), this project aims to save time and resources by up to 95%, while reducing waste generation, thereby enhancing the commercial and societal impact of drug development. Traditional process optimization in drug development is a time-consuming and expensive endeavor, often relying on trial-and-error approaches. Lead optimization and route scouting in pre-clinical drug development can take months and involve thousands of experiments, costing millions of dollars in personnel expenses alone. This project seeks to address these challenges by employing ML to guide experimental design with small datasets. Through a combination of ML and chemistry knowledge, this project aims to streamline the optimization process by suggesting only the most promising experiments and minimizing the number of failed attempts. This solution not only grants patients quicker access to medicines but also enables companies to generate earlier revenue and maintain longer market exclusivity.This Small Business Innovation Research Phase II project addresses one of the most significant challenges faced by research and development (R&D) chemists: the optimization of categorical variables in synthetic processes, specifically solvent selection. Solvents play a vital role in the chemical industry, including reaction, separation, purification, and formulation. The proper solvent can improve efficiency, reduce costs, and result in a more environmentally friendly process. Despite successful advances in ML-guided optimization and green solvent selection methodologies, available tools do not effectively combine environmental and performance parameters for simultaneous solvent selection and process optimization. This project will provide a solution for scientists across the chemical industry to leverage small data and innovative ML technologies to advance manufacturing.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.

这项小型企业创新研究（SBIR）第二阶段项目的更广泛/商业影响将改善和加速制药行业的新化学，过程和配方。通过利用机器学习的力量（ML），该项目旨在将时间和资源节省多达95％，同时减少废物产生，从而增强药物开发的商业和社会影响。药物开发中的传统过程优化是一项耗时且昂贵的努力，通常依靠反复试验。临床前药物开发中的铅优化和路线侦察可能需要数月，并且涉及数千个实验，仅花费数百万美元的人事费用。该项目旨在通过使用ML通过小型数据集指导实验设计来解决这些挑战。通过ML和化学知识的结合，该项目旨在通过仅提出最有前途的实验并最大程度地减少失败尝试的数量来简化优化过程。该解决方案不仅可以使患者更快地使用药物，而且还使公司能够产生较早的收入并保持更长的市场排他性。该小型企业创新研究阶段II阶段项目解决了研发（R＆D）化学家面临的最重大挑战之一：在合成过程中的分类变量的优化，特定于综合选择。溶剂在化学工业中起着至关重要的作用，包括反应，分离，纯化和配方。适当的溶剂可以提高效率，降低成本并导致更环保的过程。尽管在ML引导的优化和绿色溶剂选择方法方面取得了成功，但可用的工具并未有效地结合环境和性能参数，用于同时选择溶剂选择和过程优化。该项目将为整个化学工业的科学家提供一个解决方案，以利用小型数据和创新的ML技术来推进制造业。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。