  Pre-trained Language Models have emerged as promising tools for predicting
molecular properties, yet their development is in its early stages,
necessitating further research to enhance their efficacy and address challenges
such as generalization and sample efficiency. In this paper, we present a
multi-view approach that combines latent spaces derived from state-of-the-art
chemical models. Our approach relies on two pivotal elements: the embeddings
derived from MHG-GNN, which represent molecular structures as graphs, and
MoLFormer embeddings rooted in chemical language. The attention mechanism of
MoLFormer is able to identify relations between two atoms even when their
distance is far apart, while the GNN of MHG-GNN can more precisely capture
relations among multiple atoms closely located. In this work, we demonstrate
the superior performance of our proposed multi-view approach compared to
existing state-of-the-art methods, including MoLFormer-XL, which was trained on
1.1 billion molecules, particularly in intricate tasks such as predicting
clinical trial drug toxicity and inhibiting HIV replication. We assessed our
approach using six benchmark datasets from MoleculeNet, where it outperformed
competitors in five of them. Our study highlights the potential of latent space
fusion and feature integration for advancing molecular property prediction. In
this work, we use small versions of MHG-GNN and MoLFormer, which opens up an
opportunity for further improvement when our approach uses a larger-scale
dataset.
