Deep Neural Network Approach to Predict Properties of Drugs and Drug-Like Molecules

Magdalena Wiercioch, Johannes Kirchmair

Abstract: The discovery of small molecules with desirable properties is an essential issue in chemistry which could speed up much research progress in various domains such as virtual screening and drug design. Indeed, there is a series of open challenges, including building proper representations of molecules for machine learning algorithms. To address this issue, in this study we propose a deep neural network-based architecture that learns molecular representation to enhance the process of molecular properties prediction. We use 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. The performance of the proposed methodology was tested on the MoleculeNet, a standard benchmark for molecular machine learning. The results show that our method outperforms state-of-the-art models.