BaCNet: Deep Learning Accelerates Novel Antibiotic Discovery Against Resistant Pathogens

Abstract

Drug-resistant infections pose a global health challenge and necessitate the rapid development of novel antibiotics. Although high-speed and high-accuracy in silico drug discovery methods using AI have been established, only a few approaches that specifically target antibiotic development have been developed. This gap significantly limits our ability to rapidly discover effective antibacterials against emerging resistant pathogens. Here, we have developed BaCNet, an AI system that accurately predicts the binding affinity between bacterial proteins and compounds using only amino acid sequences and compound SMILES representations. Our approach integrates a protein language model with three complementary compound embedding methods, achieving high prediction accuracy and effectively maintaining performance when tested on previously unseen bacterial species. BaCNet successfully rediscovered known antibiotics and identified promising novel candidates, with molecular dynamics simulations confirming stable binding of top hits. Moreover, by integrating a compound generation and optimization system with BaCNet, we discovered novel compounds not present in existing databases with significantly enhanced predicted antibacterial activity. BaCNet represents a promising platform that could accelerate the identification of urgently needed treatments against resistant pathogens.