Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.

ABSTRACT: Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the current standard training paradigm for machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries and frequent data transmission between entities, potentially exposing proprietary information to unauthorized access or interception during storage

SUBMITTER: Chen G 

PROVIDER: S-EPMC12462486 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature