This repository provides implementations of self-supervised pre-training methods for Graph Neural Networks (GNNs), as presented in the ICLR 2020 paper. The project includes context prediction, edge prediction, and masking-based pre-training objectives. It supports pre-training GNNs on molecular graphs from chemistry and biology domains, followed by fine-tuning for downstream tasks. The implementation uses PyTorch Geometric and includes datasets for both domains.