Modeling Immunogenicity

Immunogenicity is one of the most serious problems facing the design of new biologics. In a nutshell, immunogenicity is when the patients immune system mounts a response against the introduced biologic. There are a number of reasons this can happen, such as contamination of the biologic (for example, deamidation can create isoaspartic acid "residues" on biologic, triggering an immune response). See this detailed FDA review:

https://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/SmallBusinessAssistance/UCM408709.pdf

It seems likely that deep learning could help model immunogenicity. Are there any public reports of data available?

Start by reading the linked FDA review and the issue's question about public immunogenicity data. Investigate whether public reports or datasets suitable for deep-learning models exist, and document the relevant sources, access constraints, and a clear proposed scope for modeling immunogenicity.

Written by the indexing model from the issue text.