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.
