SBIR Phase I: A physics-based machine learning platform for crystal structure prediction of small drug molecules

SBIR 第一阶段：基于物理的机器学习平台，用于小药物分子晶体结构预测

• 批准号: 2227936
• 负责人: Derek Metcalf
• 金额: $ 27.5万
• 依托单位: LAVO LIFE SCIENCES INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227936&HistoricalAwards=false
• 关键词: SBIR; Phase; physics; based; machine

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate and reduce the cost of the early stages of small molecule pharmaceutical research. The number of drugs a pharmaceutical company can bring to market is limited by the time, cost, and complexity involved in developing each drug. The research and development process typically takes around 10 years, and few drugs make it onto the market each year. This technology may be especially impactful in improving the frequency at which small molecule drugs are developed for understudied diseases, which collectively impact over 30 million Americans. By reducing the cost and time to market for new pharmaceuticals, the project could advance the industry and bring life-changing therapeutics to underserved people who are suffering from illnesses where there are presently no drug options.This project develops technologies to solve the crystal structure prediction (CSP) problem. The crystalline structure of small molecules and peptides determines many pharmacological characteristics including solubility, oral bioavailability, shelf-life stability, and toxicity. Experimental determination of the crystal structure is expensive and requires significant human labor to conduct, so a computational approach would reinvent the characterization of small molecule drugs. The proposed technical innovation combines a novel energy prediction models based on quantum chemistry with a machine learning method for efficiently sampling the vast space of possible crystal structures. The resulting technology will help pharmaceutical companies de-risk their drug development process by allowing them to analyze crystal structures computationally before having to synthesize them in the lab.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) 第一阶段项目的更广泛影响是加速并降低小分子药物研究早期阶段的成本。制药公司可以推向市场的药物数量受到开发每种药物所涉及的时间、成本和复杂性的限制。研发过程通常需要10年左右，每年上市的药物很少。这项技术对于提高针对未充分研究的疾病的小分子药物的开发频率可能特别有影响力，这些疾病总共影响了超过 3000 万美国人。通过降低新药物的成本和上市时间，该项目可以推动行业发展，并为目前没有药物选择的疾病患者带来改变生活的治疗方法。该项目开发解决晶体结构预测问题的技术（CSP）问题。小分子和肽的晶体结构决定了许多药理学特性，包括溶解度、口服生物利用度、保质期稳定性和毒性。晶体结构的实验测定成本昂贵，并且需要大量的人力来进行，因此计算方法将重新发明小分子药物的表征。所提出的技术创新将基于量子化学的新型能量预测模型与机器学习方法相结合，以有效地对可能的晶体结构的广阔空间进行采样。由此产生的技术将帮助制药公司降低药物开发过程的风险，使他们能够在实验室合成晶体结构之前进行计算分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。