Learn molecular representations from large-scale unlabeled molecules for drug discovery

TL;DR

This paper introduces MPG, a novel graph-based pre-training framework for molecular representations that leverages large-scale unlabeled data to improve drug discovery tasks.

Contribution

It presents a new self-supervised pre-training method using MolGNet on 11 million molecules, enhancing molecular representation for various drug discovery applications.

Findings

Pre-trained MolGNet captures valuable chemistry insights.

Achieves state-of-the-art results on 13 benchmark datasets.

Effective for multiple drug discovery tasks.

Abstract

How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful MolGNet model and an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemistry insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction, and drug-target interaction, involving 13 benchmark datasets. Our work demonstrates that MPG is promising to become a novel approach in the drug discovery pipeline.