  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.
