  Unlike vision and language data which usually has a unique format, molecules
can naturally be characterized using different chemical formulations. One can
view a molecule as a 2D graph or define it as a collection of atoms located in
a 3D space. For molecular representation learning, most previous works designed
neural networks only for a particular data format, making the learned models
likely to fail for other data formats. We believe a general-purpose neural
network model for chemistry should be able to handle molecular tasks across
data modalities. To achieve this goal, in this work, we develop a novel
Transformer-based Molecular model called Transformer-M, which can take
molecular data of 2D or 3D formats as input and generate meaningful semantic
representations. Using the standard Transformer as the backbone architecture,
Transformer-M develops two separated channels to encode 2D and 3D structural
information and incorporate them with the atom features in the network modules.
When the input data is in a particular format, the corresponding channel will
be activated, and the other will be disabled. By training on 2D and 3D
molecular data with properly designed supervised signals, Transformer-M
automatically learns to leverage knowledge from different data modalities and
correctly capture the representations. We conducted extensive experiments for
Transformer-M. All empirical results show that Transformer-M can simultaneously
achieve strong performance on 2D and 3D tasks, suggesting its broad
applicability. The code and models will be made publicly available at
https://github.com/lsj2408/Transformer-M.
