One Transformer Can Understand Both 2D & 3D Molecular Data

• URL: http://arxiv.org/abs/2210.01765v4
• Date: Tue, 28 Mar 2023 03:01:29 GMT
• Title: One Transformer Can Understand Both 2D & 3D Molecular Data
• Authors: Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He
• Abstract summary: We develop a novel Transformer-based Molecular model called Transformer-M. It can take molecular data of 2D or 3D formats as input and generate meaningful semantic representations. All empirical results show that Transformer-M can simultaneously achieve strong performance on 2D and 3D tasks.
• Score: 94.93514673086631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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