metadata
dataset_info:
features:
- name: cid
dtype: int64
- name: smiles
dtype: string
- name: molecule_fp
sequence:
sequence: int32
- name: selfies
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: validation
num_bytes: 5814884
num_examples: 3301
- name: test
num_bytes: 5671766
num_examples: 3300
- name: train
num_bytes: 46496406
num_examples: 26407
download_size: 15941579
dataset_size: 57983056
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: train
path: data/train-*
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling
For more information, please refer to our paper and GitHub repository.
Paper: arxiv, openreview
GitHub: 3D-MolT5
Authors: Qizhi Pei, Rui Yan, Kaiyuan Gao, Jinhua Zhu and Lijun Wu