Why Deep Models Often Cannot Beat Non-deep Counterparts on Molecular Property Prediction?

Jun Xia, Lecheng Zhang, Xiao Zhu, Stan Z. Li

Keywords: molecular representation learning

TL;DR: We comprehensively evaluate various models on molecular property prediction, explain the failure of deep models.

Abstract:

Molecular property prediction is a crucial task in the AI-driven Drug Discovery (AIDD) pipeline, which has recently gained considerable attention thanks to advances in deep learning. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 representative models (3 non-deep models and 9 deep models) on 14 molecule datasets. Through the most comprehensive study to date, we make the following key observations: \textbf{(\romannumeral 1)} Deep models are generally unable to outperform non-deep ones; \textbf{(\romannumeral 2)} The failure of deep models on MPP cannot be solely attributed to the small size of molecular datasets. What matters is the irregular molecule data pattern; \textbf{(\romannumeral 3)} In particular, tree models using molecular fingerprints as inputs tend to perform better than other competitors.
Furthermore, we conduct extensive empirical investigations into the unique patterns of molecule data and inductive biases of various models underlying these phenomena.

Submission Number: 11