Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and fetal development. The rapid identification of PB-permeable contaminants is urgently needed due to the inefficiencies of conventional cell-based transwell assays for the screening of large quantities of chemicals. Herein, we construct a PBScreen server using a multifusion deep learning (DL) model for the accurate and rapid screening of PB-permeable chemicals. This model is trained using graph convolutional networks and deep neural networks algorithms. It achieves state-of-the-art performance with an accuracy of 0.927, a false negative rate of 0.074, and an area under the receiver operating characteristic curve of 0.960. The robustness and generalization of the model as assessed using the external validation set and BeWo cell-based transwell assays demonstrate its potential for diverse applications. Our study establishes an efficient high-throughput screening tool that aids in screening PB-permeable chemicals, thereby enhancing the risk assessment of contaminants associated with key public health concerns.