UNI-MOL: A UNIVERSAL 3D MOLECULAR REPRESENTATION LEARNING FRAMEWORK

Abstract

Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction/generation. In this paper, we propose a universal 3D MRL framework, called Uni-Mol, that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol contains two pretrained models with the same SE(3) Transformer architecture: a molecular model pretrained by 209M molecular conformations; a pocket model pretrained by 3M candidate protein pocket data. Besides, Uni-Mol contains several finetuning strategies to apply the pretrained models to various downstream tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. The code, model, and data are made publicly available at

1. INTRODUCTION

Recently, representation learning (or pretraining, self-supervised learning) [1; 2; 3] has been prevailing in many applications, such as BERT [4] and GPT [5; 6; 7] in Natural Language Processing (NLP), ViT [8] in Computer Vision (CV), etc. These applications have a common characteristic: unlabeled data is abundant, while labeled data is limited. As a solution, in a typical representation learning method, one first adopts a pretraining procedure to learn a good representation from large-scale unlabeled data. Then a finetuning scheme is followed to extract more information from limited supervised data. Applications in the field of drug design share the characteristic that calls for representation learning schemes. The chemical space that a drug candidate lies in is vast, while drug-related labeled data is limited. Not surprisingly, compared with traditional molecular fingerprint-based models [9; 10], recent molecular representation learning (MRL) models perform much better in most property prediction tasks [11; 12; 13] . However, to further improve the performance and extend the application scope of existing MRL models, one is faced with a critical issue. From the perspective of life science, the properties of molecules and the effects of drugs are mostly determined by their 3D structures [14; 15] . In most current MRL methods, one starts with representing molecules as 1D sequential strings, such as SMILES [16; 17; 18] and InChI [19; 20; 21], or 2D graphs [22; 11; 23; 12; 24] . This may limit their ability to incorporate 3D information for downstream tasks. In particular, this makes it almost impossible for 3D geometry prediction or generation, such as, e.g., the prediction of proteinligand binding pose [25] . Even though there have been some recent attempts trying to leverage 3D information in MRL [26; 27] , the performance is less than optimal, possibly due to the small size of

availability

https://github.

