Predicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality. The issue of class imbalance further increases the intricacy of prediction. Different computational techniques have been presented for predicting DDI-induced ADRs in the general population; however, the area of DDI-induced pregnancy and neonatal ADRs has been underexplored. In the present work, a sparse ensemble-based computational approach is proposed that leverages SMILES strings as features, addresses high-dimensional and sparse data using Sparse Principal Component Analysis (SPCA), mitigates class imbalance with the Multilabel Synthetic Minority Oversampling Technique (MLSMOTE), and predicts pregnancy and neonatal ADRs through a stacking ensemble model. The SPCA has been evaluated for handling sparse data and shown 2.67 %–5.45 % improvement compared to PCA. The proposed stacking ensemble model has outperformed six state-of-the-art predictors regarding micro and macro scores for True Positive Rate (TPR), F1 Score, False Positive Rate (FPR), Precision, Hamming Loss, and ROC-AUC Score with 1.16%–14.94%

Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are inadequate due to ethical restrictions that prevent performing medication studies in pregnant women, leading to restricted data samples. Hence, computational techniques have been promising for ADR predictions. However, most of these techniques have focused on the general population and face the challenge of class imbalance and lack of model interpretability. In the present work, an ensemble learning-based PregAN-NET framework has been proposed that addresses the issue of class imbalance by generating synthetic data employing Conditional Tabular Generative Adversarial Network (CTGAN) and integrates neural network and gradient boosting as a Boosted Neural Ensemble (BNE) architecture to predict safe and unsafe drugs considering their adverse reactions during pregnancy. Furthermore, the SHAP method has been employed to enhance the post-hoc interpretability of the BNE architecture by analyzing the contribution of different features towards prediction. The proposed framework has been applied to chemical and biological properties from PubChem and DrugBank, along with class labels from the ADReCS database. CTGAN has been evaluated for data balancing, showing a 2% to 5% performance improvement over SMOTE. The BNE architecture has outperformed six state-of-the-art methods by achieving mean ROC-AUC scores between 77.00% and 90.00% for chemical data, 66.00% and 74.00% for biological data, and 70.00% to 75.00% for combined datasets. Further, the top 20 contributory features in prediction corresponding to the different drug properties have been identified.

The COVID-19 pandemic has underscored the critical need for diagnostic methods that are both rapid and accurate. Chest X-ray (CXR) imaging is a valuable resource for detecting respiratory illnesses, including COVID-19, but its reliance on subjective visual analysis underscores the necessity for automated solutions. This research presents an innovative approach to image enhancement, designed to enhance the effectiveness of deep convolutional neural networks (CNNs) in tasks involving multiple class classifications. To evaluate its effectiveness, five pre-trained models—‘VGG16,’ ‘VGG19,’ ‘ResNet50,’ ‘ResNet101,’ and ‘EfficientNetB0’,have been tested on an enhanced dataset comprising three balanced categories: ’COVID-19’, ’normal’, and ’viral pneumonia’, each with 1,500 X-ray images. The proposed enhancement method addresses challenges such as low contrast and poor visibility, optimizing image quality for improved classification accuracy. Among the evaluated models, EfficientNetB0 demonstrated superior performance, achieving the highest ’precision’, ’accuracy’, ’F1-score’, and ’recall’. The findings highlight the effectiveness of integrating advanced image enhancement with pre-trained models to enable reliable and efficient COVID-19 diagnosis from CXR images.

Adverse drug reactions (ADRs) during pregnancy represent a critical concern, as they can adversely affect both maternal and fetal health. However, the availability of clinical evidence on drug safety in this population has remained limited, primarily due to the ethical restrictions associated with conducting controlled trials in pregnant women. A range of computational approaches has been proposed to address this gap. Nonetheless, the majority of these methods have relied on one-dimensional or two-dimensional molecular descriptors, thereby neglecting the richer structural information contained within chemical conformers. In this work, we propose FracPool-DCNN, a novel deep learning architecture that integrates dilated convolutions with fractional max pooling to predict pregnancy-related ADRs directly from conformer images. Using a curated dataset of drugs from PubChem conformers and ADReCS-based annotations, the model has been trained and evaluated with five-fold cross-validation. FracPool-DCNN has achieved superior performance compared to nine baseline models, with a harmonic mean of 76.89%, AUPR of 77.42%, and ROC-AUC of 79.89%, while ablation studies confirm the critical contributions of fractional pooling and global average pooling. These findings highlight the promise of conformer-based deep learning for robust pregnancy drug-safety classification, offering a scalable approach to preclinical risk assessment.