Protein kinase B (PKB) acts as a central node on the PI3K kinase pathway. Constitutive activation and overexpression of PKB have been identified to involve in various cancers. However, protein kinase A (PKA) sharing high homology with PKB is essential for metabolic regulation. Therefore, specific targeting on PKB is crucial strategy in drug design and development for antitumor. Here, we had revealed the selectivity mechanism for PKB inhibitors with molecular dynamics simulation and 3D-QSAR methods. Selective inhibitors of PKB could form more hydrogen bonds and hydrophobic contacts with PKB than those with PKA. This could explain that selective inhibitor M128 is more potent to PKB than to PKA. Then, 3D-QSAR models were constructed for these selective inhibitors and evaluated by test set compounds. 3D-QSAR model comparison of PKB inhibitors and PKA inhibitors reveals possible methods to improve the selectivity of inhibitors. These models can be used to design new chemical entities and make quantitative prediction of the specific selective inhibitors before resorting to in vitro and in vivo experiment.

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD codes. Key evidence was also extracted to make our prediction more convincing and explainable.We used the Medical Information Mart for Intensive Care III (MIMIC-III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.

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