XAI-INVENT: An explainable artificial intelligence based framework for rapid discovery of novel antibiotics

Abstract

The failure of the most potent medicines to eradicate superbugs underscores the urgent need to develop new antimicrobial drugs. Antibacterial peptides (ABPs) are oligopeptides present in all multicellular organisms and serve as the first line of defense against pathogens. ABPs provide several benefits over conventional antibiotics; therefore, they have recently gained significant attention as an alternative. Finding ABPs in the laboratory is expensive and time-consuming. Therefore, wet-lab researchers use in-silico tools to discover ABPs from natural sources. The existing tools available for this purpose suffer from the limitation of being black boxes. In the present work, we developed XAI-INVENT, an explainable artificial intelligence-based framework for the rapid discovery of novel antibiotics. For building XAI-INVENT, first, the probability scores of deep learning models are fused, and then the fused scores are utilized with local interpretable model-agnostic explanations (LIME) for determining the critical amino acids. The value of performance metrics, namely Accuracy, Sensitivity, Precision, F1-Score, Specificity, and Matthews correlation coefficient obtained by the proposed framework for test data is $\approx$ 96 %, 96 %, 97 %, 96 %, 97 %, and 92 %, respectively. To help wet-lab researchers, XAI-INVENT is deployed as a web server at https://xai-invent.anvil.app/.