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

Award ID: 2325045

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.

NSF Program Director: Ela Mirowski