Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction

TL;DR

This paper introduces a framework that enhances molecular property prediction by integrating local descriptors from pretrained machine learning potentials into 3D graph neural networks, leading to improved accuracy across multiple datasets.

Contribution

The study presents a novel integration of PFP-derived local descriptors into 3D GNNs, demonstrating significant accuracy improvements in molecular property prediction tasks.

Findings

Superior accuracy on QM9 dataset for most properties.

Enhanced performance on transition metal complexes in tmQM dataset.

Method is adaptable to any 3D GNN architecture.

Abstract

In this study, we present a framework aimed at enhancing molecular property prediction through the integration of local descriptors obtained from large-scale pretrained machine learning potentials into three-dimensional graph neural networks (3D GNNs). As an illustration, we developed an EGNN-PFP model by integrating descriptors derived from the preferred potential (PFP) features, acquired through Matlantis, into an equivariant graph neural network (EGNN), and evaluated its effectiveness. When tested on the QM9 dataset, comprising small organic molecules, the proposed model demonstrated superior accuracy compared to both the original EGNN models and the baseline models without PFP-derived descriptors for 11 out of the 12 molecular properties. Furthermore, when evaluated on the tmQM dataset, which encompasses transition metal complexes, notable enhancements in performance were observed across all five target properties, indicating the significance of the local atomic environment surrounding transition metals. In essence, the proposed methodology is adaptable to any 3D GNN architecture, and further enhancements in prediction accuracy are anticipated when integrated with continually evolving GNN architectures.