metadata

license: gpl-3.0
datasets:
  - alimotahharynia/approved_drug_target
language:
  - en
base_model:
  - openai-community/gpt2
  - liyuesen/druggpt
pipeline_tag: text-generation
library_name: transformers
tags:
  - chemistry
  - biology
  - medical

DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback

DrugGen is a GPT-2 based model specialized for generating drug-like SMILES structures based on protein sequence. The model leverages the characteristics of approved drug targets and has been trained through both supervised fine-tuning and reinforcement learning techniques to enhance its ability to generate chemically valid, safe, and effective structures.

Model Details

Model Name: DrugGen
Training Paradigm: Supervised Fine-Tuning (SFT) + Proximal Policy Optimization (PPO)
Input: Protein Sequence
Output: SMILES Structure
Training Libraries: Hugging Face’s transformers and Transformer Reinforcement Learning (TRL)
Model Sources: liyuesen/druggpt

How to Get Started with the Model

DrugGen can be used via command-line interface (CLI) or integration into Python scripts.

Installation

Clone the repository and navigate to its directory

git clone https://github.com/mahsasheikh/DrugGen.git
cd DrugGen

Install dependencies

pip3 install -r requirements.txt

Command-Line Interface

DrugGen provides a CLI to generate SMILES structures based on UniProt IDs, protein sequences, or both.

Generating SMILES Structures

python3 drugGen_generator_cli.py --uniprot_ids <UniProt_IDs> --sequences <Protein_Sequences> --num_generated <Number_of_Structures> --output_file <Output_File_Name>

Example Command

python3 drugGen_generator_cli.py --uniprot_ids P12821 P37231 --sequences "MGAASGRRGPGLLLPLPLLLLLPPQPALALDPGLQPGNFSADEAGAQLFAQSYNSSAEQVLFQSVAASWAHDTNITAENARRQEEAALLSQEFAEAWGQKAKELYEPIWQNFTDPQLRRIIGAVRTLGSANLPLAKRQQYNALLSNMSRIYSTAKVCLPNKTATCWSLDPDLTNILASSRSYAMLLFAWEGWHNAAGIPLKPLYEDFTALSNEAYKQDGFTDTGAYWRSWYNSPTFEDDLEHLYQQLEPLYLNLHAFVRRALHRRYGDRYINLRGPIPAHLLGDMWAQSWENIYDMVVPFPDKPNLDVTSTMLQQGWNATHMFRVAEEFFTSLELSPMPPEFWEGSMLEKPADGREVVCHASAWDFYNRKDFRIKQCTRVTMDQLSTVHHEMGHIQYYLQYKDLPVSLRRGANPGFHEAIGDVLALSVSTPEHLHKIGLLDRVTNDTESDINYLLKMALEKIAFLPFGYLVDQWRWGVFSGRTPPSRYNFDWWYLRTKYQGICPPVTRNETHFDAGAKFHVPNVTPYIRYFVSFVLQFQFHEALCKEAGYEGPLHQCDIYRSTKAGAKLRKVLQAGSSRPWQEVLKDMVGLDALDAQPLLKYFQPVTQWLQEQNQQNGEVLGWPEYQWHPPLPDNYPEGIDLVTDEAEASKFVEEYDRTSQVVWNEYAEANWNYNTNITTETSKILLQKNMQIANHTLKYGTQARKFDVNQLQNTTIKRIIKKVQDLERAALPAQELEEYNKILLDMETTYSVATVCHPNGSCLQLEPDLTNVMATSRKYEDLLWAWEGWRDKAGRAILQFYPKYVELINQAARLNGYVDAGDSWRSMYETPSLEQDLERLFQELQPLYLNLHAYVRRALHRHYGAQHINLEGPIPAHLLGNMWAQTWSNIYDLVVPFPSAPSMDTTEAMLKQGWTPRRMFKEADDFFTSLGLLPVPPEFWNKSMLEKPTDGREVVCHASAWDFYNGKDFRIKQCTTVNLEDLVVAHHEMGHIQYFMQYKDLPVALREGANPGFHEAIGDVLALSVSTPKHLHSLNLLSSEGGSDEHDINFLMKMALDKIAFIPFSYLVDQWRWRVFDGSITKENYNQEWWSLRLKYQGLCPPVPRTQGDFDPGAKFHIPSSVPYIRYFVSFIIQFQFHEALCQAAGHTGPLHKCDIYQSKEAGQRLATAMKLGFSRPWPEAMQLITGQPNMSASAMLSYFKPLLDWLRTENELHGEKLGWPQYNWTPNSARSEGPLPDSGRVSFLGLDLDAQQARVGQWLLLFLGIALLVATLGLSQRLFSIRHRSLHRHSHGPQFGSEVELRHS" --num_generated 10 --output_file g_smiles_test.txt

Parameters

uniprot_ids: Space-separated UniProt IDs.
sequences: Space-seperated protein sequences in string format.
num_generated: Number of unique SMILES structures to generate.
output_file: Name of the output file to save the generated structures.

Python Integration

# Example call for inference using only sequences
from drugGen_generator import run_inference
run_inference(
    sequences=[ "MGAASGRRGPGLLLPLPLLLLLPPQPALALDPGLQPGNFSADEAGAQLFAQSYNSSAEQVLFQSVAASWAHDTNITAENARRQEEAALLSQEFAEAWGQKAKELYEPIWQNFTDPQLRRIIGAVRTLGSANLPLAKRQQYNALLSNMSRIYSTAKVCLPNKTATCWSLDPDLTNILASSRSYAMLLFAWEGWHNAAGIPLKPLYEDFTALSNEAYKQDGFTDTGAYWRSWYNSPTFEDDLEHLYQQLEPLYLNLHAFVRRALHRRYGDRYINLRGPIPAHLLGDMWAQSWENIYDMVVPFPDKPNLDVTSTMLQQGWNATHMFRVAEEFFTSLELSPMPPEFWEGSMLEKPADGREVVCHASAWDFYNRKDFRIKQCTRVTMDQLSTVHHEMGHIQYYLQYKDLPVSLRRGANPGFHEAIGDVLALSVSTPEHLHKIGLLDRVTNDTESDINYLLKMALEKIAFLPFGYLVDQWRWGVFSGRTPPSRYNFDWWYLRTKYQGICPPVTRNETHFDAGAKFHVPNVTPYIRYFVSFVLQFQFHEALCKEAGYEGPLHQCDIYRSTKAGAKLRKVLQAGSSRPWQEVLKDMVGLDALDAQPLLKYFQPVTQWLQEQNQQNGEVLGWPEYQWHPPLPDNYPEGIDLVTDEAEASKFVEEYDRTSQVVWNEYAEANWNYNTNITTETSKILLQKNMQIANHTLKYGTQARKFDVNQLQNTTIKRIIKKVQDLERAALPAQELEEYNKILLDMETTYSVATVCHPNGSCLQLEPDLTNVMATSRKYEDLLWAWEGWRDKAGRAILQFYPKYVELINQAARLNGYVDAGDSWRSMYETPSLEQDLERLFQELQPLYLNLHAYVRRALHRHYGAQHINLEGPIPAHLLGNMWAQTWSNIYDLVVPFPSAPSMDTTEAMLKQGWTPRRMFKEADDFFTSLGLLPVPPEFWNKSMLEKPTDGREVVCHASAWDFYNGKDFRIKQCTTVNLEDLVVAHHEMGHIQYFMQYKDLPVALREGANPGFHEAIGDVLALSVSTPKHLHSLNLLSSEGGSDEHDINFLMKMALDKIAFIPFSYLVDQWRWRVFDGSITKENYNQEWWSLRLKYQGLCPPVPRTQGDFDPGAKFHIPSSVPYIRYFVSFIIQFQFHEALCQAAGHTGPLHKCDIYQSKEAGQRLATAMKLGFSRPWPEAMQLITGQPNMSASAMLSYFKPLLDWLRTENELHGEKLGWPQYNWTPNSARSEGPLPDSGRVSFLGLDLDAQQARVGQWLLLFLGIALLVATLGLSQRLFSIRHRSLHRHSHGPQFGSEVELRHS"],
    num_generated=10,
    output_file="output_SMILES.txt"
)

# Example call for inference using only UniProt IDs
from drugGen_generator import run_inference
run_inference(
    uniprot_ids=["P12821", "P37231"],
    num_generated=10,
    output_file="output_SMILES.txt"
)

# Example call for inference using both UniProt IDs and sequences
run_inference(
    sequences=["MGAASGRRGPGLLLPLPLLLLLPPQPALALDPGLQPGNFSADEAGAQLFAQSYNSSAEQVLFQSVAASWAHDTNITAENARRQEEAALLSQEFAEAWGQKAKELYEPIWQNFTDPQLRRIIGAVRTLGSANLPLAKRQQYNALLSNMSRIYSTAKVCLPNKTATCWSLDPDLTNILASSRSYAMLLFAWEGWHNAAGIPLKPLYEDFTALSNEAYKQDGFTDTGAYWRSWYNSPTFEDDLEHLYQQLEPLYLNLHAFVRRALHRRYGDRYINLRGPIPAHLLGDMWAQSWENIYDMVVPFPDKPNLDVTSTMLQQGWNATHMFRVAEEFFTSLELSPMPPEFWEGSMLEKPADGREVVCHASAWDFYNRKDFRIKQCTRVTMDQLSTVHHEMGHIQYYLQYKDLPVSLRRGANPGFHEAIGDVLALSVSTPEHLHKIGLLDRVTNDTESDINYLLKMALEKIAFLPFGYLVDQWRWGVFSGRTPPSRYNFDWWYLRTKYQGICPPVTRNETHFDAGAKFHVPNVTPYIRYFVSFVLQFQFHEALCKEAGYEGPLHQCDIYRSTKAGAKLRKVLQAGSSRPWQEVLKDMVGLDALDAQPLLKYFQPVTQWLQEQNQQNGEVLGWPEYQWHPPLPDNYPEGIDLVTDEAEASKFVEEYDRTSQVVWNEYAEANWNYNTNITTETSKILLQKNMQIANHTLKYGTQARKFDVNQLQNTTIKRIIKKVQDLERAALPAQELEEYNKILLDMETTYSVATVCHPNGSCLQLEPDLTNVMATSRKYEDLLWAWEGWRDKAGRAILQFYPKYVELINQAARLNGYVDAGDSWRSMYETPSLEQDLERLFQELQPLYLNLHAYVRRALHRHYGAQHINLEGPIPAHLLGNMWAQTWSNIYDLVVPFPSAPSMDTTEAMLKQGWTPRRMFKEADDFFTSLGLLPVPPEFWNKSMLEKPTDGREVVCHASAWDFYNGKDFRIKQCTTVNLEDLVVAHHEMGHIQYFMQYKDLPVALREGANPGFHEAIGDVLALSVSTPKHLHSLNLLSSEGGSDEHDINFLMKMALDKIAFIPFSYLVDQWRWRVFDGSITKENYNQEWWSLRLKYQGLCPPVPRTQGDFDPGAKFHIPSSVPYIRYFVSFIIQFQFHEALCQAAGHTGPLHKCDIYQSKEAGQRLATAMKLGFSRPWPEAMQLITGQPNMSASAMLSYFKPLLDWLRTENELHGEKLGWPQYNWTPNSARSEGPLPDSGRVSFLGLDLDAQQARVGQWLLLFLGIALLVATLGLSQRLFSIRHRSLHRHSHGPQFGSEVELRHS"], 
    uniprot_ids=["P12821", "P37231"], 
    num_generated=10, 
    output_file="output_SMILES.txt"
)

Training Details

Training Data

alimotahharynia/approved_drug_target

This dataset contains approved SMILES-protein sequences pairs data. It was used to train the model for generating SMILES strings.

Training Procedure

Training regime: fp32

Supervised Fine-Tuning

DrugGen was initially trained using supervised fine-tuning on a curated dataset of approved drug targets.

Training: validation sets (ratio of 8:2)
sft_config
- num_train_epochs= 5
- per_device_train_batch_size= 8
- per_device_eval_batch_size= 8
- evaluation_strategy="steps"
- save_strategy="epoch"
- eval_steps=50
- logging_steps=25
- logging_strategy="steps"
- do_eval=True
- do_train=True
- learning_rate=5e-4
- adam_epsilon=1e-08
- warmup_steps=100
- eval steps=50
- dataloader_drop_last=True
- save_safetensors=False
- max_seq_length=768
AdamW optimizer
- lr=5e-4
- eps=1e-08
scheduler
- get_linear_schedule_with_warmup

Proximal Policy Optimization

Rollout: Generates a response based on an input query. Generation parameters include:
- do_sample=True
- top_k=9
- max_length=1024
- top_p=0.9
- bos_token_id=tokenizer.bos_token_id
- eos_token_id=tokenizer.eos_token_id
- pad_token_id=tokenizer.pad_token_id
- num_return_sequences=10

In each epoch, generation continued until 30 unique small molecules were generated for each target.

Evaluation: A reward function include:
- Binding affinity predictor: "Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers was (PLAPT)"
- Customized invalid structure assessor: Based on RDKit library
- A multiplicative penalty of "0.7" when a generated SMILES matched a molecule present in the approved SMILES dataset.
Optimization:
ppo_config
- mini_batch_size=8
- batch_size=240
- learning_rate=1.41e-5
- use_score_scaling=True
- use_score_norm=True

Prompts with a tensor size greater than 768 were omitted, resulting in 2053 sequences (98.09% of the initial dataset).

Citation

If you use this model in your research, please cite our paper:

@misc{sheikholeslami2024druggenadvancingdrugdiscovery,
      title={DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback}, 
      author={Mahsa Sheikholeslami and Navid Mazrouei and Yousof Gheisari and Afshin Fasihi and Matin Irajpour and Ali Motahharynia},
      year={2024},
      eprint={2411.14157},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2411.14157}, 
}