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.