MoTSE: an interpretable task similarity estimator for small molecular property prediction tasks

Abstract

Understanding the molecular properties (e.g., physical, chemical or physiological characteristics and biological activities) of small molecules plays essential roles in biomedical researches. The accumulating amount of datasets has enabled the development of data-driven computational methods, especially the machine learning based methods, to address the molecular property prediction tasks. Due to the high cost of obtaining experimental labels, the datasets of individual tasks generally contain limited amount of data, which inspired the application of transfer learning to boost the performance of the molecular property prediction tasks. Our analyses revealed that simultaneously considering similar tasks, rather than randomly chosen ones, can significantly improve the performance of transfer learning in this field. To provide accurate estimation of task similarity, we proposed an effective and interpretable computational tool, named Molecular Tasks Similarity Estimator (MoTSE). By extracting task-related local and global knowledge from pretrained graph neural networks (GNNs), MoTSE projects individual tasks into a latent space and measures the distance between the embedded vectors to derive the task similarity estimation and thus enhance the molecular prediction results. We have validated that the task similarity estimated by MoTSE can serve as a useful guidance to design a more accurate transfer learning strategy for molecular property prediction. Experimental results showed that such a strategy greatly outperformed baseline methods including training from scratch and multitask learning. Moreover, MoTSE can provide interpretability for the estimated task similarity, through visualizing the important loci in the molecules attributed by the attribution method employed in MoTSE. In summary, MoTSE can provide an accurate method for estimating the molecular property task similarity for effective transfer learning, with good interpretability for the learned chemical or biological insights underlying the intrinsic principles of the task similarity.

Leveraging transformers‐based language models in proteome bioinformatics

“…However, recent studies have used transformer‐based NLP models to predict the binding affinity of drug molecules to target proteins based on their chemical structures and other properties. There have been numerous pre‐training methods proposed for predicting molecular properties, including SMILES‐BERT [86], GROVER [87], and KPGT [88]. Furthermore, Liu et al.…”

MF-SuP-pKa: multi-fidelity modeling with subgraph pooling mechanism for pKa prediction

“…The target of the pre-training is to learn the QSPR between various chemical structures and the predicted pKa values, and the fine-tuning stage is aimed to migrate the predicted values to the experimental values. Distinct from selfsupervised GNN pre-training[22,23], the computational pKa and the experimental pKa are strongly correlated, thus avoiding the potential of negative transfer.…”