Datasets:
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---
license: mit
task_categories:
- graph-ml
tags:
- chemistry
- molecular-biology
- drug-discovery
- multi-modal
dataset_info:
features:
- name: edge_index
list:
list: int64
- name: edge_attr
list:
list: int64
- name: x
list:
list: int64
- name: ba_edge_index
list:
list: int64
- name: ba_edge_attr
list:
list: float64
- name: fra_edge_index
list:
list: int64
- name: fra_edge_attr
list:
list: int64
- name: cluster_idx
list: int64
- name: bafra_edge_index
list:
list: int64
- name: bafra_edge_attr
list:
list: float64
- name: smiles
dtype: string
splits:
- name: train
num_bytes: 17772414767
num_examples: 1551232
- name: validation
num_bytes: 454862268
num_examples: 39775
download_size: 1889271320
dataset_size: 18227277035
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# MuMo Pretraining Dataset
- 📄 Paper: [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://huggingface.co/papers/2510.23640)
- 💻 Code: [https://github.com/selmiss/MuMo](https://github.com/selmiss/MuMo)
- 📬 Contact:
- Zihao Jing: zjing29@uwo.ca | Wechat: A2016A315214 | Instagram: nobeljing25
- Pingzhao Hu: phu49@uwo.ca
## Abstract
Multimodal molecular models often suffer from 3D conformer unreliability and modality collapse, limiting their robustness and generalization. We propose MuMo, a structured multimodal fusion framework that addresses these challenges in molecular representation through two key strategies. To reduce the instability of conformer-dependent fusion, we design a Structured Fusion Pipeline (SFP) that combines 2D topology and 3D geometry into a unified and stable structural prior. To mitigate modality collapse caused by naive fusion, we introduce a Progressive Injection (PI) mechanism that asymmetrically integrates this prior into the sequence stream, preserving modality-specific modeling while enabling cross-modal enrichment. Built on a state space backbone, MuMo supports long-range dependency modeling and robust information propagation. Across 29 benchmark tasks from Therapeutics Data Commons (TDC) and MoleculeNet, MuMo achieves an average improvement of 2.7% over the best-performing baseline on each task, ranking first on 22 of them, including a 27% improvement on the LD50 task. These results validate its robustness to 3D conformer noise and the effectiveness of multimodal fusion in molecular representation. The code is available at: this http URL .
## Dataset Overview
- Source: filtered ChEMBL (~1.6M molecules)
- Purpose: language-style pretraining over SMILES with graph/geometry supervision
- Processing: generated using `preprocess/mol3d_processor.py`
- Splits: `train` (≈1.55M), `validation` (≈39.8K)
You can load this dataset directly via the Hugging Face Datasets API or via our training scripts with `--dataset_name`.
## Data Schema (per example)
- `smiles` (string): canonical SMILES string
- Graph keys (2D topology and basic chemistry):
- `x`: node feature matrix (list of lists)
- `edge_index`: 2×E edge indices (list of two lists of int)
- `edge_attr`: edge feature matrix (list of lists)
- Fragment-level keys (BRICS-based):
- `fra_edge_index`: fragment connectivity indices (list of lists of int)
- `fra_edge_attr`: fragment edge features (list of lists)
- Geometry-level keys:
- `ba_edge_index`: geometry-based connections (list of lists of int)
- `ba_edge_attr`: features for geometry connections (list of lists)
- Geometry–fragment keys:
- `bafra_edge_index`: geometry fragment connectivity (list of lists of int)
- `bafra_edge_attr`: features for geometry fragments (list of lists)
- `cluster_idx` (list of int): fragment membership index per atom (which fragment each atom belongs to)
Notes:
- Shapes and dtypes may be adapted by downstream collators; values are stored as lists for portability.
- All lists are serialized for JSONL storage and converted to tensors during training.
## Usage
Python (Datasets):
```python
from datasets import load_dataset
ds = load_dataset("zihaojing/MuMo-Pretraining")
print(ds)
example = ds["train"][0]
print(example.keys())
```
Training script (Transformers):
```bash
deepspeed train/pretrain.py \
--dataset_name zihaojing/MuMo-Pretraining \
--do_train --do_eval \
...
```
## Processing Pipeline
We use `preprocess/mol3d_processor.py` to derive graph and geometry features from SMILES:
- Atom features, bonds, and 2D topology populate `x`, `edge_index`, `edge_attr`.
- BRICS-based fragmentation provides `fra_edge_index`, `fra_edge_attr`, and `cluster_idx`.
- Geometry connections and fragment geometry provide `ba_edge_index`, `ba_edge_attr`, `bafra_edge_index`, `bafra_edge_attr`.
## Citation
If you find this work useful, please cite:
Zihao Jing, Yan Sun, Yanyi Li, Sugitha Janarthanan, Alana Deng, and Pingzhao Hu. "MuMo: Multimodal Molecular Representation Learning via Structural Fusion and Progressive Injection." In Advances in Neural Information Processing Systems (NeurIPS), 2025. ([paper](https://huggingface.co/papers/2510.23640))
```bibtex
@inproceedings{jing2025mumo,
title = {MuMo: Multimodal Molecular Representation Learning via Structural Fusion and Progressive Injection},
author = {Jing, Zihao and Sun, Yan and Li, Yan Yi and Janarthanan, Sugitha and Deng, Alana and Hu, Pingzhao},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2025}
}
```
## License
MIT |