Ngo, J. C.; Schoonenberg, V. A. C.; Nandakumar, R.; Wu, X.; Sher, F.

Conventional structure-based drug design has high clinical failure rates due to the disconnect where binding affinity does not guarantee safe functional modulation. To bridge this gap, we present CRISPRtile, a cloud-based platform for function-based drug design. By deriving library coverage optimization equations and leveraging AI to correct CRISPR guide biases, we generated toxicity and functional landscapes with over threefold error reduction compared to conventional methods. These maps bypass error and orders of magnitude higher computational cost in structure-based pipelines by enabling AI prediction of drug interaction directly from nontoxic functional sequences, while predicting brain penetration with benchmark leading performance. We demonstrate CRISPRtile by mapping the NLRP3 inflammasome and identifying FDA approved drugs with previously unrecognized ability to modulate it, revealing strategies to amplify or inhibit our immune response to homeostatic perturbations. These advances establish a generalizable strategy for the systematic discovery of safe functional modulators.