Aladdin Healthcare Technologies SE had successfully validated its deep learning algorithms (Message Passing Neural Network) for drug discovery. Aladdin's technology has been validated last week by the International Joint Conferences on Artificial Intelligence. Aladdin's team of experts developed a new deep learning-based graph model for molecular representation. The successful results of Aladdin's deep learning algorithms have been accepted by the 29. International Joint Conference on AI among 4,717 valid submissions. The overall acceptance rate was only 12.6%. Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction, virtual screening and drug design. Deep learning methods by using Graph neural networks, especially Message Passing Neural Networks (MPNN) and its variants, have recently made remarkable achievements in drug molecular modeling. Albeit powerful, the one-sided focus on atom (node) or bond (edge) information of existing MPNN methods leads to insufficient representations of the attributed molecular graphs. Aladdin has now developed a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel.