System and method for molecular property prediction using edge-conditioned graph attention neural network

Abstract

This disclosure relates generally to a system and method for molecular property prediction. The conventional methods for molecular property prediction suffer from inherent limitation to effectively encapsulate the characteristics of the molecular graph. Moreover, the known methods are computationally intensive, thereby leading to non-performance in real-time scenarios. The disclosed method includes performing self-attention on the nodes of a molecular graph of different sized neighborhood, and further performing a shared attention mechanism across the nodes of the molecular graphs to compute attention coefficients using and Edge-condition graph attention neural network (EC-GAT). The EC-GAT effectively utilizes the edge characteristics in the molecular graph for molecular property prediction.