Abstract The large-sized CRISPR Cas proteins have hindered the effective use of CRISPR-mediated gene editing in human therapeutics due to the lack of delivery strategies for these large-sized proteins to reach their target locations. To seek solutions to this challenge, multiple efforts have been reported to search for the smaller-sized Cas proteins in nature. Recent successes in artificial intelligence (AI) application in the biological field demonstrated the power of AI in solving biological problems and brought a new perspective to address this challenge. In this application, we propose to develop a proof- of-concept technology to adapt the AI algorithms used in protein structure prediction and strategic board games to develop novel protein design tools to minimize the Cas protein size while maintaining its function. Our objectives are to develop a novel protein design technology to optimize the size of protein and develop experimentally-validated artificial mini-Cas proteins. We hypothesize that the artificial mini- Cas proteins with guided double-strand DNA cleavage function can be designed using AI technologies. To test this hypothesis, we propose to test the feasibility of developing this mini-Cas-design technology with two approaches. In Aim 1, we will use the sequence-based methods with the generative and attention-based neural networks to design mini-Cas proteins. In Aim 2, the structure-based semi- unsupervised reinforcement learning will be used for the technology development. The top candidates of designed mini-Cas protein will be evaluated by molecular dynamics simulations followed by the biochemical and cell-based assays. Our proposed technology will have the great potential to advance the technical field of protein design by providing tools to optimize the size of proteins, shifting the paradigm of the CRISPR research field from “searching from nature” to “designing in the lab”, and delivering the first artificially designed Cas proteins validated in biochemical and cell-based assays to address the CRISPR delivery challenge.