The rise of antibiotic resistance poses a significant global health threat, necessitating innovative solutions like phage therapy. Phages, viruses that target specific bacteria, offer a promising alternative due to their inexhaustible evolution potential, specificity and synergy with most antibiotics. However, two main challenges hinder their efficacy: the time-intensive process of identifying suitable phages for infections and the rapid development of bacterial resistance. This PhD project addresses these challenges through an evolution-aware machine learning framework. The approach leverages protein language models, phage-host interaction classifiers and evolutionary algorithms to design improved receptor-binding proteins, which are critical for phage infectivity. The project is divided into three work packages that together make the framework to design next-generation phage therapies.