B-cell lymphoma-extra large (Bcl-xl) is a mitochondrial transmembrane protein that acts as an anti-apoptotic protein by sequestering the apoptosis-inducing proteins Bim, Bak, and Bad. This prevents the release of cytochrome c from the mitochondria, preventing activation of apoptosis pathways. Higher levels of Bcl-xl expression are commonly found in cancer cells. This contributes to the prevention of apoptosis in cancer cells, allowing them to proliferate uncontrollably. Bcl-xl is an incredibly important target for cancer therapeutics. A Bcl-xl binder would inhibit the interaction between Bcl-xl and apoptosis inducing proteins, allowing cancer cells to undergo apoptosis. In my research, I am using deep learning methods to design cyclic peptides that bind to Bcl-xl. To design the binders, I used RFDiffusion - a generative diffusion model - to produce thousands of cyclic peptide binder scaffolds bound to Bcl-xl. I then used a sequence-based deep learning tool to generate multiple sequences for each backbone design. The resulting binders were computationally validated with the highly accurate, machine-learning-based structure prediction tools AlphaFold and RoseTTAFold. Of the 40000 generated cyclic peptides, 2052 were predicted to bind to Bcl-xl based on standard metrics. Along with their excellent metrics, these designs show a high structural similarity and binding location to the known Bcl-xl binders Bim, Bak, and Bad. The designs were clustered by backbone into 350 unique clusters. We synthesized the top designs and identified which peptides display binding to Bcl-xl through a Homogeneous Time-Resolved Fluorescence (HTRF) assay. A successful Bcl-xl binder has the potential to serve as the basis for an effective and affordable cancer therapy.