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.

本文提出了一种多视图方法，将来自最先进化学模型的潜在空间进行结合，其中使用了基于 MHG-GNN 的嵌入（将分子结构表示为图形）和根植于化学语言的 MoLFormer 嵌入。我们通过在六个基准数据集上评估该方法，证明了我们所提出的多视图方法在预测临床试验药物毒性和抑制 HIV 复制等复杂任务上，优于现有的最先进方法，包括在 11 亿个分子上进行训练的 MoLFormer-XL。研究结果突显了潜在空间融合和特征整合在推进分子属性预测方面的潜力。