Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by 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 by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas 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 at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

细菌生物膜是人类传染性疾病的来源，与抗生素耐药性密切相关。铜绿假单胞菌是一种广泛存在的多重耐药细菌，与多种医院获得性感染有关。近年来，开发了能够抑制铜绿假单胞菌的新药通过干扰其形成生物膜的能力已成为药物发现中有前途的策略。识别能够干扰生物膜形成的分子是困难的，但通过合理地提高它们的活性来进一步开发这些分子尤其具有挑战性，因为它需要了解被抑制的特定蛋白质靶标。这项工作描述了机器学习多技术共识工作流程的发展，以预测具有确认的抑制铜绿假单胞菌生物膜形成活性的分子的蛋白质靶标。它使用一个专门的数据库，其中包含与铜绿假单胞菌形成生物膜有关的所有已知目标。ChEMBL 上可用的经实验证实的抑制剂连同化学描述符被用作九种不同分类模型组合的输入特征，从而产生了一种共识方法来预测最可能的配体靶标。已实施的算法可在 GNU 通用公共许可证 (GPL) 第 3 版许可下从 https://github.com/BioSIM-Research-Group/TargIDe 免费获得，并且可以随着更多数据的可用而轻松改进。