Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance.Pseudomonas aeruginosais a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibitPseudomonas aeruginosaby interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation byPseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation byPseudomonas aeruginosa.The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available athttps://github.com/BioSIM-Research-Group/TargIDeunder licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10822-023-00505-5.