Molecular property prediction is essential for drug discovery. In recent
years, deep learning methods have been introduced to this area and achieved
state-of-the-art performances. However, most of existing methods ignore the
intrinsic relations between molecular properties which can be utilized to
improve the performances of corresponding prediction tasks. In this paper, we
propose a new approach, namely Physics properties Enhanced Molecular Property
prediction (PEMP), to utilize relations between molecular properties revealed
by previous physics theory and physical chemistry studies. Specifically, we
enhance the training of the chemical and physiological property predictors with
related physics property prediction tasks. We design two different methods for
PEMP, respectively based on multi-task learning and transfer learning. Both
methods include a model-agnostic molecule representation module and a property
prediction module. In our implementation, we adopt both the state-of-the-art
molecule embedding models under the supervised learning paradigm and the
pretraining paradigm as the molecule representation module of PEMP,
respectively. Experimental results on public benchmark MoleculeNet show that
the proposed methods have the ability to outperform corresponding
state-of-the-art models.

本文介绍了 PEMP，即利用物理化学理论和研究揭示的化合物间关系来增强化学和生理特性预测模型的新方法，该方法包括多任务学习和迁移学习两种模型构建方法，并在公共基准 MoleculeNet 上表现出超越对应最先进模型的能力。