SBIR Phase I: Artificial Intelligence Platform for Peptide Drug Discovery

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的商业影响是改进了多肽疗法的药物发现过程。多肽药物的关键治疗领域是糖尿病、肥胖症和癌症。抗体通常是一种治疗选择，但可能很昂贵，给患者及其家人带来了巨大的压力。合成肽药物的生产成本更低，同时具有类似的特异性和低毒性。这项提议的技术可以提高临床前Lead开发的效率；节省20%的时间将导致单个发现计划节省420万美元。这个小型企业创新研究(SBIR)第一阶段项目旨在展示和实验验证新型神经网络框架准确预测具有抗炎活性的多肽的能力。这项拟议的创新通过使用新颖的数据编码和创新的模型架构，解决了目前应用于肽数据集的人工智能方法的根本局限性。该项目将涉及进一步开发体系结构，并纳入有效的数据扩充和转移学习战略，以有效地利用小数据集。此外，模型预测的多肽变体将通过生成湿实验室数据和评估现实生活中的性能来广泛验证。将开发可视化有前景的多肽模式的工具，以使广泛采用人工智能方法所需的模型成为可能。这项提议的成功完成将产生新的抗炎先导肽和一个独特的深度学习框架，以加速和改进多肽药物的发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is an improved drug discovery process for peptide therapeutics. The key therapeutic areas for peptide drugs are diabetes, obesity and cancer. Antibodies are often a treatment of choice but can be expensive, putting significant strain on patients and their families. Synthetic peptide drugs can be cheaper to produce while having similar specificity and low toxicity. This proposal’s technology could improve the efficiency of pre-clinical lead development; a time savings of 20% would result in $4.2 M in savings in a single discovery program. This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate and experimentally validate the ability of a novel neural network framework to accurately predict peptides with anti-inflammatory activity. The proposed innovation addresses fundamental limitations of current artificial intelligence approaches, as applied to peptide datasets, via use of novel data encoding and innovative model architectures. This project will involve further development of the architectures and incorporation of effective data augmentation and transfer learning strategies to effectively leverage small datasets. Moreover, the model-predicted peptide variants will be extensively validated by generating wet-lab data and evaluating real-life performance. Tools to visualize the promising peptide patterns will be developed to enable models necessary for widespread adoption of artificial intelligence approaches. The successful completion of this proposal will result in novel anti-inflammatory lead peptides and a unique deep learning framework to accelerate and improve peptide drug discovery.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.