README.md

---
license: gpl-3.0
language:
- en
pretty_name: SELFormerMM
size_categories:
- 1M<n<10M
task_categories:
- tabular-classification
- tabular-regression
- feature-extraction
- graph-ml
tags:
- chemistry
- cheminformatics
- drug-discovery
- molecular-property-prediction
- multimodal
- selfies
- chembl
- moleculenet
- representation-learning
configs:
- config_name: bace
  data_files: finetuning_datasets/classification/bace/bace.csv
- config_name: bbbp
  data_files: finetuning_datasets/classification/bbbp/bbbp.csv
- config_name: hiv
  data_files: finetuning_datasets/classification/hiv/hiv.csv
- config_name: sider
  data_files: finetuning_datasets/classification/sider/sider.csv
- config_name: tox21
  data_files: finetuning_datasets/classification/tox21/tox21.csv
- config_name: esol
  data_files: finetuning_datasets/regression/esol/esol.csv
- config_name: freesolv
  data_files: finetuning_datasets/regression/freesolv/freesolv.csv
- config_name: lipo
  data_files: finetuning_datasets/regression/lipo/lipo.csv
- config_name: pdbbind_full
  data_files: finetuning_datasets/regression/pdbbind_full/pdbbind_full.csv
---

# SELFormerMM

Multimodal molecular representation data for **SELFormerMM** — an extension of
[SELFormer](https://github.com/HUBioDataLab/SELFormer) that aligns four complementary views of a
molecule in a shared embedding space: **SELFIES** sequences, **3D/structural graphs**,
**textual descriptions**, and **knowledge-graph** context, trained with multimodal supervised
contrastive learning on ~2.9M molecules.

Paper: **[SELFormerMM: multimodal molecular representation learning via SELFIES, structure, text, and
knowledge graph integration](https://doi.org/10.1093/bioinformatics/btag451)** (*Bioinformatics*, 2026)

Code: **https://github.com/HUBioDataLab/SELFormerMM**

This repository bundles everything needed to reproduce or build on SELFormerMM:

| Component | What it is |
|---|---|
| `pretraining_datasets/` | 2,854,815 ChEMBL v36 molecules with precomputed structure, text and KG embeddings, plus the heterogeneous KG itself |
| `finetuning_datasets/` | 9 downstream MoleculeNet benchmarks (BACE, BBBP, HIV, SIDER, Tox21, ESOL, FreeSolv, Lipophilicity, PDBbind), each with a metadata CSV and an aligned `.npz` of the three non-SELFIES modalities |
| `models/SELFormerMM/` | The multimodal RoBERTa backbone pretrained on the data above |
| `models/finetuned/` | One fine-tuned checkpoint per downstream task |
| `models/DMGI/` | The DMGI encoder used to produce the knowledge-graph embeddings |

---

Every artifact in this repository is row-aligned. Row `i` of a metadata CSV describes the same
molecule as row `i` of every `.npy` / `.npz` array that accompanies it. 

A molecule that has no description, no KG node, or a structure that failed RDKit validation gets a
zero vector in that modality rather than being dropped. Downstream code is expected to treat an
all-zero row as "modality missing".

If you regenerate any modality yourself, you must preserve this ordering or the model will silently
receive mismatched inputs.

---

## Pretraining data

`pretraining_datasets/` — 2,854,815 drug-like molecules drawn from ChEMBL v36.

| File | Shape / size | Description |
|---|---|---|
| `pretraining_dataset_meta.csv` | 2,854,815 rows, 1.7 GB | Identifiers, structures and raw descriptions |
| `graph_embeddings.npy` | `(2854815, 512)` float32 | Uni-Mol `[CLS]` representations |
| `text_embeddings.npy` | `(2854815, 768)` float32 | SciBERT mean-pooled description embeddings |
| `kg_embeddings.npy` | `(2854815, 128)` float32 | DMGI node embeddings |
| `selformermm_kg_heterodata.pt` | 2.2 GB | The [CROssBAR v2](https://crossbarv2.hubiodatalab.com/) subgraph as a PyTorch Geometric `HeteroData` object |

### `pretraining_dataset_meta.csv` columns

| Column | Description |
|---|---|
| `selfies` | SELFIES string — the model's primary input |
| `chembl_id` | ChEMBL identifier, e.g. `CHEMBL153534` |
| `canonical_smiles` | Canonical SMILES from ChEMBL |
| `standard_inchi` | InChI |
| `standard_inchi_key` | InChIKey |
| `cid` | PubChem CID, when a mapping exists |
| `Description` | Textual description mapped from the [M3-20M](https://github.com/bz99bz/M-3) dataset |
| `kg_compound_node_idx` | Index of this compound's node in `selformermm_kg_heterodata.pt`, when present in the [CROssBAR v2](https://crossbarv2.hubiodatalab.com/) subgraph |


### Modality coverage

| Modality | Molecules with a non-zero vector | Coverage | Source |
|---|---:|---:|---|
| SELFIES | 2,854,815 | 100.00% | ChEMBL v36 SMILES, converted with `selfies` |
| Structure (Uni-Mol) | 2,854,734 | 100.00% | 81 molecules failed RDKit validation and were excluded |
| Text (description) | 492,702 | 17.26% | Mapped to M3-20M |
| Knowledge graph | 725,908 | 25.43% | CROssBAR v2 subgraph |


### The knowledge graph

`selformermm_kg_heterodata.pt` is a focused subgraph of **CROssBAR v2** (which itself integrates 34
heterogeneous biomedical sources), restricted to the node and edge types most directly tied to
molecular properties — compounds, proteins, drugs and genes. It contains **1,402,102 nodes** and
**4,424,830 relationships**. Node indices referenced by `kg_compound_node_idx` are into the
`Compound` node store, whose `mapping` attribute keys are of the form `chembl:CHEMBL6206`.

### How the embeddings were produced

Every non-SELFIES encoder is a **frozen pretrained checkpoint** — only the SELFIES encoder and the
projection networks were trained.

- **Structure — 512-d.** [Uni-Mol](https://github.com/deepmodeling/Uni-Mol) (`unimol_tools.UniMolRepr`,
  `data_type="molecule"`, hydrogens retained) applied to the canonical SMILES; the `cls_repr` vector
  is taken as the molecule representation.
- **Text — 768-d.** [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) (`allenai/scibert_scivocab_uncased`),
  max length 512, mean-pooling over the last hidden state of the `Description` field.
- **Knowledge graph — 128-d.** A [DMGI](https://doi.org/10.1609/aaai.v34i04.5985) (Deep Multiplex Graph Infomax) encoder — one `GCNConv` per
  relation type with a bilinear discriminator against a per-relation graph summary — trained on
  `selformermm_kg_heterodata.pt`. Per-relation node embeddings are averaged to give the final vector.
  The checkpoint is at `models/DMGI/dmgi_model.pt`.

All three matrices are mean-centered and L2-normalized over the non-zero rows; zero rows (missing
modality) are left untouched so they stay exactly zero.

---

## Fine-tuning datasets

`finetuning_datasets/` — nine downstream benchmarks, each a directory containing `<task>.csv` and
`<task>_embs.npz`. The `.npz` holds three arrays, `graph` `(n, 512)`, `text` `(n, 768)` and
`kg` `(n, 128)`, row-aligned to the CSV and produced by the same encoders as the pretraining data.

### Classification

| Task | Molecules | Label column(s) | Notes |
|---|---:|---|---|
| `bace` | 1,513 | `Class` | Binary — human β-secretase 1 inhibition. 691 positives (45.7%) |
| `bbbp` | 2,039 | `p_np` | Binary — blood–brain barrier permeability. 1,560 positives (76.5%) |
| `hiv` | 41,127 | `HIV_active` | Binary — HIV replication inhibition. 1,443 positives (3.5%) |
| `sider` | 1,427 | 27 columns | Multi-label — adverse drug reactions by MedDRA system organ class |
| `tox21` | 7,831 | 12 columns | Multi-label — nuclear-receptor and stress-response toxicity assays |

### Regression

| Task | Molecules | Target column | Range (mean) |
|---|---:|---|---|
| `esol` | 1,128 | `measured log solubility in mols per litre` | −11.60 … 1.58 (−3.05) |
| `freesolv` | 642 | `freesolv` | −25.47 … 3.43 (−3.80) |
| `lipo` | 4,200 | `lipo` | −1.50 … 4.50 (2.19) |
| `pdbbind_full` | 9,880 | `-logKd/Ki` | 0.40 … 9.30 (6.24) |

Besides its label column(s), every task CSV carries `selfies`, `smiles`, and the mapping columns
`standard_inchi_key`, `cid`, `Description`, `chembl_id`, `canonical_smiles`, `kg_compound_node_idx`.
`esol` additionally keeps the original MoleculeNet descriptor columns.

### Per-task modality coverage

Percentage of rows with a non-zero vector in each modality:

| Task | n | Graph | Text | KG | All four |
|---|---:|---:|---:|---:|---:|
| `bace` | 1,513 | 100.00% | 7.40% | 13.09% | 2.12% |
| `bbbp` | 2,039 | 100.00% | 67.48% | 2.70% | 1.86% |
| `hiv` | 41,127 | 99.98% | 14.23% | 0.78% | 0.28% |
| `sider` | 1,427 | 91.03% | 80.66% | 1.89% | 1.40% |
| `tox21` | 7,831 | 99.90% | 76.76% | 4.61% | 3.75% |
| `esol` | 1,128 | 100.00% | 88.30% | 3.55% | 3.46% |
| `freesolv` | 642 | 100.00% | 91.59% | 4.83% | 4.67% |
| `lipo` | 4,200 | 100.00% | 32.19% | 24.05% | 5.24% |
| `pdbbind_full` | 9,880 | 99.91% | 23.66% | 4.96% | 1.47% |

SELFIES coverage is 100% for every task.

### Splits

The CSVs are shipped unsplit. The paper's protocol is 80/10/10 train/validation/test, with
**scaffold splitting for the binary classification tasks** (BACE, BBBP, HIV) and **random splitting**
for the multilabel and regression tasks. `train_finetuning.py` reproduces this via `--use_scaffold`,
`--train_frac`/`--val_frac`/`--test_frac` and `--seed`; reported numbers are means over three seeds.

---

## Models

### `models/SELFormerMM/` — pretrained multimodal backbone

A RoBERTa encoder over SELFIES (12 layers, hidden size 768, 4 attention heads, vocabulary 800,
max position 514), initialized from [SELFormer](https://huggingface.co/HUBioDataLab/SELFormer), with
three parallel projection MLPs that map the structure (512-d), text (768-d) and KG (128-d) vectors
into the same 768-d space. Each projection expands and contracts the dimension through
`×4 → ×6 → ×6 → ×4 → ×1` of the hidden size with `LayerNorm` + `ReLU` between layers.
246,245,376 trainable parameters in total.

Trained with **SINCERE loss** (τ = 0.07), a supervised extension of InfoNCE that accommodates
multiple positive views per molecule. The three auxiliary encoders stay frozen; only the SELFIES
encoder and the projections are updated. Includes the SELFIES BPE tokenizer.

### `models/finetuned/<task>/`

One checkpoint per downstream task (`bace`, `bbbp`, `esol`, `freesolv`, `hiv`, `lipo`,
`pdbbind_full`, `sider`, `tox21`). Each directory holds the task tokenizer plus `model.pt`, a
checkpoint dict with `backbone` and `head` state dicts, loadable by `predict.py` in the code
repository.

### `models/DMGI/dmgi_model.pt`

The DMGI encoder checkpoint that generated `kg_embeddings.npy`. 

### Reported performance

Selected results from the paper, mean ± standard deviation over three random seeds. ROC-AUC for
classification (higher is better), RMSE for regression (lower is better). See the paper for the full
table and the unimodal/multimodal baselines.

| Task | Metric | SELFormerMM |
|---|---|---|
| SIDER | ROC-AUC | 0.751 ± 0.013 |
| BACE | ROC-AUC | 0.779 ± 0.021 |
| BBBP | ROC-AUC | 0.947 ± 0.005 |
| HIV | ROC-AUC | 0.788 ± 0.025 |
| Tox21 | ROC-AUC | 0.845 ± 0.012 |
| ESOL | RMSE | 0.672 ± 0.060 |
| FreeSolv | RMSE | 1.070 ± 0.065 |
| Lipophilicity | RMSE | 0.624 ± 0.049 |
| PDBbind | RMSE | 1.310 ± 0.040 |

---

## Usage

### Browse a downstream task in the viewer

```python
from datasets import load_dataset

bbbp = load_dataset("HUBioDataLab/SELFormerMM", "bbbp")
```

The named configs above expose the nine task CSVs. The embedding arrays are not part of these
configs — `datasets` cannot align `.npz` files row-wise — so download them directly.

### Load a task with all four modalities

```python
import numpy as np, pandas as pd
from huggingface_hub import hf_hub_download

REPO = "HUBioDataLab/SELFormerMM"
meta = hf_hub_download(REPO, "finetuning_datasets/classification/bbbp/bbbp.csv", repo_type="dataset")
embs = hf_hub_download(REPO, "finetuning_datasets/classification/bbbp/bbbp_embs.npz", repo_type="dataset")

df = pd.read_csv(meta)
z = np.load(embs)
graph, text, kg = z["graph"], z["text"], z["kg"]

assert len(df) == len(graph) == len(text) == len(kg)      # row-aligned
has_kg = np.linalg.norm(kg, axis=1) != 0                  # zero vector == modality missing
```

### Fine-tune from the pretrained backbone

```bash
huggingface-cli download HUBioDataLab/SELFormerMM --repo-type dataset \
  --include "models/SELFormerMM/*" "finetuning_datasets/classification/bbbp/*" --local-dir ./selformermm

python train_finetuning.py \
  --model_path ./selformermm/models/SELFormerMM \
  --dataset_meta_csv ./selformermm/finetuning_datasets/classification/bbbp/bbbp.csv \
  --dataset_embs_npz ./selformermm/finetuning_datasets/classification/bbbp/bbbp_embs.npz \
  --task_type binary --label_column p_np --num_labels 1 \
  --use_scaffold 1 --epochs 50 --backbone_lr 1e-5 --head_lr 1e-4 \
  --save_dir ./runs/bbbp
```

### Predict with a released fine-tuned checkpoint

```bash
python predict.py \
  --model_dir ./selformermm/models/finetuned/bbbp \
  --input_meta_csv ./selformermm/finetuning_datasets/classification/bbbp/bbbp.csv \
  --input_embs_npz ./selformermm/finetuning_datasets/classification/bbbp/bbbp_embs.npz \
  --task_type binary --num_labels 1 --label_column p_np \
  --output_csv ./bbbp_predictions.csv
```

### Pretrain on your own corpus

Supply a SELFIES CSV plus a `.npy` per modality with identical row ordering; substitute a
zero matrix of the right shape for any modality you do not have. See `train_pretraining.py` and the
`generate_*_embeddings.py` scripts in the code repository.

---

## Citation

If you use this data, please cite:

```bibtex
@article{ulusoy2026selformermm,
  title   = {SELFormerMM: multimodal molecular representation learning via SELFIES,
             structure, text, and knowledge graph integration},
  author  = {Ulusoy, Erva and Bostanc{\i}, {\c{S}}evval and Deniz, Bora Engin and Do{\u{g}}an, Tunca},
  journal = {Bioinformatics},
  volume  = {42},
  number  = {Supplement\_2},
  pages   = {btag451},
  year    = {2026},
  doi     = {10.1093/bioinformatics/btag451}
}
```

## License

**GNU General Public License v3.0 or later.**

> This program is free software: you can redistribute it and/or modify it under the terms of the GNU
> General Public License as published by the Free Software Foundation, either version 3 of the
> License, or (at your option) any later version.

Data redistributed here remains subject to the terms of its original sources (ChEMBL, M3-20M,
CROssBARv2, MoleculeNet, PDBbind).

## Contact

[HU Biological Data Science Lab](https://github.com/HUBioDataLab) — open an issue on the
[code repository](https://github.com/HUBioDataLab/SELFormerMM).