README.md

---
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