Prediction of minimum inhibitory concentration of antibacterial peptides using geometric graph networks and dynamic loss functions.

Antibiotic resistance has reduced the effectiveness of traditional antibiotics for public health needs. Antibacterial peptides (ABPs) hold clinical value due to their inherent antimicrobial activity and resistance to degradation. Existing deep learning approaches for predicting the minimum inhibitory concentration (MIC) of ABPs typically rely on a single modality or a single scale, limiting their ability to capture ABP complexity. Moreover, these methods cover only a narrow range of bacterial species. To address these challenges, we propose the geometric graph network (GGN)-ABPMIC model, which integrates multi-scale structural features at the atomic and residue scales with peptide sequence features. These features are aggregated through a GGN enhanced with geometric vector perceptrons. To enrich the feature representation, we also incorporate descriptors that quantify the physicochemical properties of ABPs and fuse them with the GGN-processed features. The model predicts the MIC values for ABPs across datasets spanning 10 bacterial species. For training and robustness, we introduce a multi-stage dynamic-weight hybrid loss that combines mean-squared error (MSE), the Huber loss, and a contrastive learning loss. Across the 10 species, GGN-ABPMIC achieves a mean MSE of 0.221, a mean R$^2$ of 0.564, and a mean Pearson correlation coefficient of 0.749, outperforming existing approaches. Additional validate via case studies on supplementary Escherichia coli ABP sequences indicates that the MIC predictions of GGN-ABPMIC are closer to the true values, demonstrating strong predictive performance and generalization for ABP activity. The data and codes for GGN-ABPMIC are available at https://github.com/LiJinYu1231/GGN-ABPMIC/tree/master.