计算机科学
代表(政治)
限制
变压器
机器学习
人工智能
范围(计算机科学)
训练集
编码(集合论)
三维模型
理论计算机科学
程序设计语言
工程类
政治
电气工程
政治学
电压
集合(抽象数据类型)
机械工程
法学
作者
Gengmo Zhou,Zhifeng Gao,Qiankun Ding,Hang Zheng,Hongteng Xu,Zhewei Wei,Linfeng Zhang,Guolin Ke
标识
DOI:10.26434/chemrxiv-2022-jjm0j-v4
摘要
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 https://github.com/dptech-corp/Uni-Mol.
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