Update README.md (#1)

- Update README.md (6e6e49ef6a08706f398ec79f1ee8d439d5b2144c)


Co-authored-by: Ali Motahharynia <[email protected]>

README.md

@@ -3,202 +3,123 @@ base_model: meta-llama/Llama-3.1-8B-Instruct
 library_name: peft
 datasets:
 - Moreza009/drug_approval_prediction
+license: apache-2.0
+pipeline_tag: text-classification
+tags:
+- medical
+- biology
+- chemistry
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
+DrugReasoner is an AI-powered system for predicting drug approval outcomes using reasoning-augmented Large Language Models (LLMs) and molecular feature analysis. By combining advanced machine learning with interpretable reasoning, DrugReasoner provides transparent predictions that can accelerate pharmaceutical research and development.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
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-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+-  Model Name: DrugReasoner
+-  Training Paradigm: Group Relative Policy Optimization (GRPO)
+-  Input: SMILES Structure
+-  Output: Drug approval prediction + Rational of approval or unapproval + Confidence score
+-  Training Libraries: Hugging Face’s transformers, Transformer Reinforcement Learning (TRL), and Parameter-efficient fine-tuning (PEFT)
+-  Model Sources: meta-llama/Llama-3.1-8B-Instruct
 
 ## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.15.1
+-  To use **DrugReasoner**, you must first request access to the base model [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on Hugging Face by providing your contact information. Once access is granted, you can run DrugReasoner either through the command-line interface (CLI) or integrate it directly into your Python workflows.
+
+### Prerequisites
+
+- Python 3.8 or higher
+- CUDA-compatible GPU (GPU is required for inference.)
+- Git
+
+### Setup Instructions
+
+1. **Clone the repository**
+   ```bash
+   git clone [email protected]:mohammad-gh009/DrugReasoner.git
+   cd DrugReasoner
+   ```
+
+2. **Create and activate virtual environment**
+
+   **Windows:**
+   ```bash
+   python -m venv myenv
+   myenv\Scripts\activate
+   ```
+
+   **Mac/Linux:**
+   ```bash
+   python -m venv myenv
+   source myenv/bin/activate
+   ```
+
+3. **Install dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+### Batch Inference
+
+**Data Requirements:**
+- Place your dataset in the `datasets/` folder
+- Ensure your CSV file contains columns named `SMILES` and `Label`
+- SMILES column should contain molecular structure strings
+- Label column should contain the ground truth labels (if available)
+
+```bash
+cd DrugReasoner/src
+python batch_inference.py --input ../datasets/test_processed.csv --output ../outputs/results.csv
+```
+
+**Example dataset format:**
+```csv
+SMILES,Label
+"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",1
+"CC1=CC=C(C=C1)C(=O)O",0
+"CCN(CC)CCCC(C)NC1=C2C=CC(=CC2=NC=C1)CF3",1
+```
+
+### Single Molecule Inference
+```bash
+python inference.py \
+    --smiles "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O" "CC1=CC=C(C=C1)C(=O)O" \
+    --output results.csv \
+    --top-k 9 \
+    --top-p 0.9 \
+    --max-length 2048 \
+    --temperature 1.0
+```
+
+### Python API Usage
+```python
+from inference import DrugReasoner
+
+predictor = DrugReasoner()
+
+results = predictor.predict_molecules(
+    smiles_list=["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"],
+    save_path="results.csv",
+    print_results=True,
+    top_k=9,
+    top_p=0.9,
+    max_length=2048,
+    temperature=1.0
+)
+```
+
+#### Performance Evaluation
+
+Evaluate model performance using a CSV file with `y_pred` and `y_true` columns:
+
+```bash
+python utils.py --evaluate "path_to_results.csv"
+```
+
+## Citation
+If you use DrugReasoner in your research, please cite our paper:
+```
+Ghaffarzadeh-Esfahani, M., Motahharynia, A*., Yousefian, N., Mazrouei, N., Ghaisari, J., & Gheisari, Y. 
+DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model.
+arXiv:2508.18579 (2025). https://doi.org/10.48550/arXiv.2508.18579
+```