---
tags:
- biology
- small-molecules
- single-cell-genes
- drug-discovery
- ibm
- mammal
- pytorch

library_name: biomed-multi-alignment
license: apache-2.0
base_model:
- ibm/biomed.omics.bl.sm.ma-ted-458m
---

Accurate prediction of drug-target binding affinity is essential in the early stages of drug discovery.  
This is an example of finetuning ibm/biomed.omics.bl.sm-ted-400 the task.  
Prediction of binding affinities using pKd, the negative logarithm of the dissociation constant, which reflects the strength of the interaction between a small molecule (drug) and a protein (target).  
The expected inputs for the model are the amino acid sequence of the target and the SMILES representation of the drug.  

The benchmark used for fine-tuning defined on: `https://tdcommons.ai/multi_pred_tasks/dti/`  
We also harmonize the values using data.harmonize_affinities(mode = 'max_affinity') and transforming to log-scale.  
By default, we are using Drug+Target cold-split, as provided by tdcommons.


## Model Summary

- **Developers:** IBM Research
- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
- **Paper:** https://arxiv.org/abs/2410.22367
- **Release Date**: Oct 28th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Usage

Using `ibm/biomed.omics.bl.sm.ma-ted-458m` requires installing https://github.com/BiomedSciAI/biomed-multi-alignment

```
pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git#egg=mammal[examples]
```

A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-458m`:
```python
import os
from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp

from mammal.examples.dti_bindingdb_kd.task import DtiBindingdbKdTask
from mammal.keys import CLS_PRED, SCORES
from mammal.model import Mammal

# input
target_seq = "NLMKRCTRGFRKLGKCTTLEEEKCKTLYPRGQCTCSDSKMNTHSCDCKSC"
drug_seq = "CC(=O)NCCC1=CNc2c1cc(OC)cc2"

# Load Model
model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd")
model.eval()

# Load Tokenizer
tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd")

# convert to MAMMAL style
sample_dict = {"target_seq": target_seq, "drug_seq": drug_seq}
sample_dict = DtiBindingdbKdTask.data_preprocessing(
    sample_dict=sample_dict,
    tokenizer_op=tokenizer_op,
    target_sequence_key="target_seq",
    drug_sequence_key="drug_seq",
    norm_y_mean=None,
    norm_y_std=None,
    device=model.device,
)

# forward pass - encoder_only mode which supports scalar predictions
batch_dict = model.forward_encoder_only([sample_dict])

# Post-process the model's output
batch_dict = DtiBindingdbKdTask.process_model_output(
    batch_dict,
    scalars_preds_processed_key="model.out.dti_bindingdb_kd",
    norm_y_mean=5.79384684128215,
    norm_y_std=1.33808027428196,
)
ans = {
    "model.out.dti_bindingdb_kd": float(batch_dict["model.out.dti_bindingdb_kd"][0])
}

# Print prediction
print(f"{ans=}")
```

For more advanced usage, see our detailed example at: on `https://github.com/BiomedSciAI/biomed-multi-alignment` 


## Citation

If you found our work useful, please consider giving a star to the repo and cite our paper:
```
@misc{shoshan2024mammalmolecularaligned,
      title={MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language}, 
      author={Yoel Shoshan and Moshiko Raboh and Michal Ozery-Flato and Vadim Ratner and Alex Golts and Jeffrey K. Weber and Ella Barkan and Simona Rabinovici-Cohen and Sagi Polaczek and Ido Amos and Ben Shapira and Liam Hazan and Matan Ninio and Sivan Ravid and Michael M. Danziger and Joseph A. Morrone and Parthasarathy Suryanarayanan and Michal Rosen-Zvi and Efrat Hexter},
      year={2024},
      eprint={2410.22367},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2410.22367}, 
}
```