The　increasing　prevalence　of　antibiotic　resistance　in　Cutibacterium　acnes　(C.　acnes)　requires　the　search　for　alternative　therapeutic　strategies.　Antimicrobial　peptides　(AMPs)　offer　a　promising　avenue　for　the　development　of　new　treatments　targeting　C.　acnes.　In　this　study,　to　design　peptides　with　the　specific　inhibitory　activity　against　C.　acnes,　we　employed　a　deep　learning　pipeline　with　generators　and　classifiers,　using　transfer　learning　and　pretrained　protein　embeddings,　trained　on　publicly　available　data.　To　enhance　the　training　data　specific　to　C.　acnes　inhibition,　we　constructed　a　phylogenetic　tree.　A　panel　of　42　novel　generated　linear　peptides　was　then　synthesized　and　experimentally　evaluated　for　their　antimicrobial　selectivity　and　activity.　Five　of　them　demonstrated　their　high　potency　and　selectivity　against　C.　acnes　with　MIC　of　2-4　mu　g/mL.　Our　findings　highlight　the　potential　of　these　designed　peptides　as　promising　candidates　for　anti-acne　therapeutics　and　demonstrate　the　power　of　computational　approaches　for　the　rational　design　of　targeted　antimicrobial　peptides.