DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction

In this study we propose the Deep Neural Network Properties Predictor (DNN-PP) which is a deep neural network-based architecture that learns molecular representation to enhance the process of molecular properties prediction. This work's main novelty is the use of two separate blocks of operations, where each block learns a representation. Then the two latent feature vectors are combined and fed into a few dense layers ended by a regression or classification layer. Furthermore, our DNN-PP employs a stacked attention mechanism and proposes a collection of atomic and bond features to provide additional insights into a molecular structure. Therefore, while various approaches for molecular properties prediction have already been developed, our architecture can capture detailed structure-property relationship information. The performance of the proposed DNN-PP was tested on a standard benchmark for molecular machine learning. The results show that our method outperforms state-of-the-art models. Take for instance hydration free energies prediction, where DNN-PP obtains the smallest predictive error, i.e., 0.73 on the test set, while the best competitive approach reports an error of 1.07 on unseen data (lower is better).