  Inspired by its success in natural language processing and computer vision,
pre-training has attracted substantial attention in cheminformatics and
bioinformatics, especially for molecule based tasks. A molecule can be
represented by either a graph (where atoms are connected by bonds) or a SMILES
sequence (where depth-first-search is applied to the molecular graph with
specific rules). Existing works on molecule pre-training use either graph
representations only or SMILES representations only. In this work, we propose
to leverage both the representations and design a new pre-training algorithm,
dual-view molecule pre-training (briefly, DMP), that can effectively combine
the strengths of both types of molecule representations. The model of DMP
consists of two branches: a Transformer branch that takes the SMILES sequence
of a molecule as input, and a GNN branch that takes a molecular graph as input.
The training of DMP contains three tasks: (1) predicting masked tokens in a
SMILES sequence by the Transformer branch, (2) predicting masked atoms in a
molecular graph by the GNN branch, and (3) maximizing the consistency between
the two high-level representations output by the Transformer and GNN branches
separately. After pre-training, we can use either the Transformer branch (this
one is recommended according to empirical results), the GNN branch, or both for
downstream tasks. DMP is tested on nine molecular property prediction tasks and
achieves state-of-the-art performances on seven of them. Furthermore, we test
DMP on three retrosynthesis tasks and achieve state-of-the-art results on them.

Quantitative Methods;Machine Learning