MycoPermeNet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student Self-Distillation.

Machine learning (ML) techniques offer a promising path for accelerating antibiotic discovery by computationally predicting and optimizing desirable pharmaceutical properties of molecules. One difficult case for drug discovery is tuberculosis (TB), the disease caused byMycobacterium tuberculosis, a bacterium with intrinsic resistance to many antibiotics. The unique outer membrane of M. tuberculosis, called the mycomembrane, is thought to contribute to intrinsic antibiotic resistance by establishing a permeability barrier. While recent ML works have attempted to predict the permeability of compounds through the mycomembrane, the scarcity of labeled data has led to approaches that struggle to generalize. In this work, we propose a robust two-stage model, MycoPermeNet-v2, to predict compound permeability through the mycomembrane of M. tuberculosis. MycoPermeNet-v2 fuses molecular descriptors with learned graph-based embeddings and incorporates Noisy Student self-disTillation (NST) to improve performance even when labeled data is limited. Compared to prior work, we achieve significantly higher performance (RMSE from 0.755 ± 0.024 to 0.719 ± 0.021, adjusted p < 0.0001). We systematically evaluate the robustness of the overall model across different data split strategies, as well as the contribution of each component in the proposed feature fusion and NST framework. Furthermore, the interpretability analysis shows that the learned representations capture chemically meaningful features relevant to M. tuberculosis permeability. We show the generalizability of our approach over multiple models and physical chemistry property prediction tasks, demonstrating strong applicability in data-constrained molecular learning scenarios.