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.

该小企业创新研究 (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新化学品、工艺和配方的开发。通过利用机器学习 (ML) 的力量，该项目旨在节省高达 95% 的时间和资源，同时减少废物的产生，从而增强药物开发的商业和社会影响。药物开发中的传统工艺优化是一项耗时且昂贵的工作，通常依赖于试错方法。临床前药物开发中的先导化合物优化和路线探索可能需要数月时间，涉及数千次实验，仅人员费用就花费数百万美元。该项目旨在通过使用机器学习来指导小数据集的实验设计来应对这些挑战。通过结合机器学习和化学知识，该项目旨在通过仅建议最有希望的实验并最大程度地减少失败尝试的次数来简化优化过程。该解决方案不仅使患者能够更快地获得药物，还使公司能够更早地产生收入并保持更长时间的市场独占权。这个小型企业创新研究第二阶段项目解决了研发 (R&D) 化学家面临的最重大挑战之一：合成过程中分类变量的优化，特别是溶剂的选择。溶剂在化学工业中发挥着至关重要的作用，包括反应、分离、纯化和配制。合适的溶剂可以提高效率、降低成本并实现更加环保的工艺。尽管机器学习引导的优化和绿色溶剂选择方法取得了成功，但可用的工具无法有效地将环境和性能参数结合起来以同时进行溶剂选择和工艺优化。该项目将为整个化学行业的科学家提供一个解决方案，以利用小数据和创新的机器学习技术来推进制造业。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。