Graph Residual based Method for Molecular Property Prediction

TL;DR

This paper introduces ECRGNN, a novel graph neural network approach that predicts molecular properties directly from molecular graph structures derived from SMILES data, improving accuracy and efficiency.

Contribution

The work presents a new deep learning model, ECRGNN, that directly predicts molecular properties from graph-structured data, enhancing generalization and inference speed over traditional methods.

Findings

ECRGNN outperforms benchmark models on standard datasets.

The method effectively predicts both regression and classification properties.

The approach demonstrates improved inference time and accuracy.

Abstract

Machine learning-driven methods for property prediction have been of deep interest. However, much work remains to be done to improve the generalization ability, accuracy, and inference time for critical applications. The traditional machine learning models predict properties based on the features extracted from the molecules, which are often not easily available. In this work, a novel Deep Learning method, the Edge Conditioned Residual Graph Neural Network (ECRGNN), has been applied, allowing us to predict properties directly only the Graph-based structures of the molecules. SMILES (Simplified Molecular Input Line Entry System) representation of the molecules has been used in the present study as input data format, which has been further converted into a graph database, which constitutes the training data. This manuscript highlights a detailed description of the novel GRU-based methodology, ECRGNN, to map the inputs that have been used. Emphasis is placed on highlighting both the regressive property and the classification efficacy of the same. A detailed description of the Variational Autoencoder (VAE) and the end-to-end learning method used for multi-class multi-label property prediction has been provided as well. The results have been compared with standard benchmark datasets as well as some newly developed datasets. All performance metrics that have been used have been clearly defined, and their reason for choice.