---
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](https://arxiv.org/abs/2406.05797), [openreview](https://openreview.net/forum?id=eGqQyTAbXC)

GitHub: [3D-MolT5](https://github.com/QizhiPei/3D-MolT5)

Authors: *Qizhi Pei, Rui Yan, Kaiyuan Gao, Jinhua Zhu and Lijun Wu*