Design And Implementation Of Deep Learning Based Model For Drug Discovery

Abstract

One of the most important tasks for artificial intelligence-assisted molecular design is the prediction of physicochemical qualities from molecular structures. To meet this problem, an increasing number of Graph Neural Networks (GNNs) have been proposed. By including more information in molecules, these models expand their expressive power while unavoidably increasing their computational complexity. This work, seeks to create a powerful and effective novel GNN for molecular structures. By first representing each molecule as a two-layer multiplex graph, one layer of which only contains local connections that primarily capture covalent interactions and the other layer of which contains global connections that can simulate non-covalent interactions, the proposed molecular mechanicsdriven approach to accomplishing this goal. Then, in order to balance the trade-off between expression strength and computing complexity, a corresponding message passing module is proposed for each layer. This work proposed the novel GNN-based multiplex molecular graph which outperform when it was verified using a dataset for big protein-ligand complexes and tiny compounds. newline newline newlineKeywords: Neural network, GNN, AI, deep learning, drug discovery, molecules newline newline