Researchers are exploring various methods to improve molecular property prediction, including the use of geometry-aware lightweight convolutional networks. Other approaches being investigated involve graph neural networks, adaptive edge-aware graph convolutional networks, and geometric transport for domain-policy reinforcement learning. These different methods aim to enhance the representation and prediction of molecular properties, with some focusing on specific applications such as drug-target interaction and protein-protein complex prediction.