NIH R01 · 2025
SCH: Graph-String Transformer and Reinforcement Learning for Design of Cancer Theranostics
This research aims to develop and validate an innovative artificial intelligence (AI)-driven approach for accelerating the discovery of dual-targeted cancer theranostic agents to overcome therapy resistance. The investigation focuses on developing bispecific small molecule inhibitory conjugates (BsSMICs) targeting both stearoyl-CoA desaturase-1 (SCD-1) and fatty acid desaturase 2 (FADS2), two critical enzymes in cancer cell fatty acid metabolism. The specific aims are to: (1) develop an AI algorithm integrating graph-string transformers and reinforcement learning to generate synthesizable molecules for highly specific target-based cancer theranostics, and (2) implement the AI algorithm to…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.