Drug discovery is a notoriously lengthy and costly process which is often described as finding a needle in a haystack. In this case, the haystack comprises on the order of 10 60 -10 100 synthetically feasible molecules , of which we need to find an ideal compound which satisfies multiple criteria including bioactivity, drug metabolism and pharmacokinetic (DMPK) profile, synthetic accessibility, etc. To reduce the number of compounds synthesized and improve efficiency during the drug development, Quantitative Structure Activity Relationship (QSAR) analysis has been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable mathematical or statistical model for prediction of the activities of new chemical entities. After Hansch proposed the QSAR concept , engineering molecular descriptors to build accurate models for the prediction of various properties has become the standard approach to QSAR modelling . Over the past few years, deep learning-based methods have achieved significant progress in molecular property prediction and de novo drug design. Recent results have shown that deep learning-based models can learn task specific molecular features directly from molecular structure and the conventional feature selection process is no longer needed. Among these efforts, Graphbased neural networks (GNNs) have particularly emerged as a powerful method for solving the property prediction problem. For example GAT , MPNN and recently attention based transformer models were shown to be able to provide more accurate prediction than traditional machine learning models. Among these models, usually atomic embedding representing atom features is learned from data and fused to form the graph embedding which is then correlated with endpoint properties. However, there is much less effort in exploring the atom pair embedding for building prediction model. Recently published UniMol model utilized atom pair embedding to build models, it learned atom pair representations based on 3D conformations and a resource demanding pre-training on millions of 3D conformation of unlabelled molecules was needed for achieving good performance on 2D based molecular property prediction tasks as well as 3D information based protein-ligand affinity prediction task. Here, we proposed a novel methodology called atom pair attention network (APAN) for learning atom pair embedding and applied it in multiple prediction tasks. One type of task was to build models on various benchmark datasets existing in MolecularNet database. These benchmark datasets contain purely 2D molecular information and have widely been used for developing novel deep learning algorithms. In contrast to the 3D based UniMol model, we built models using the atom pair embedding without doing pre-training on large number of unlabelled data and they out-performed previous state-of-the-art models.