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README.md

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-license: mit
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+# **PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers**
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/plapt-protein-ligand-binding-affinity/protein-ligand-affinity-prediction-on-csar)](https://paperswithcode.com/sota/protein-ligand-affinity-prediction-on-csar?p=plapt-protein-ligand-binding-affinity)
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/plapt-protein-ligand-binding-affinity/protein-ligand-affinity-prediction-on-pdbbind)](https://paperswithcode.com/sota/protein-ligand-affinity-prediction-on-pdbbind?p=plapt-protein-ligand-binding-affinity)
+
+This is the official code repository for PLAPT, a state-of-the-art protein-ligand binding affinity predictor. [Preprint](https://doi.org/10.1101/2024.02.08.575577)
+
+
+
+
+### Abstract
+Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently. We introduce PLAPT, a novel model leveraging transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.
+
+![PLAPT Architecture](https://github.com/trrt-good/WELP-PLAPT/blob/main/Diagrams/PLAPT.png)
+
+---
+# Usage
+---
+## Plapt CLI
+
+Plapt CLI is a command-line interface for the Plapt Python package, designed for predicting affinities using sequences and SMILES strings. This tool is user-friendly and offers flexibility in output formats and file handling.
+
+### Prerequisites
+
+Before using Plapt CLI, you need to have the following installed:
+- Python (Download and install from [python.org](https://www.python.org/))
+- Git (Download and install from [git-scm.com](https://git-scm.com/)) - Alternatively, you can download the repository as a ZIP file.
+
+### Installation
+
+To install Plapt CLI, you can clone the repository from GitHub:
+
+```bash
+git clone https://github.com/trrt-good/WELP-PLAPT.git
+cd WELP-PLAPT
+```
+
+If you prefer not to use Git, download the ZIP file of the repository and extract it to a desired location.
+
+Once you have the repository on your local machine, install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+(Optional) If you are using a virtual environment, activate it before installing the dependencies:
+
+```bash
+source /path/to/your/venv/bin/activate
+```
+
+### Running the Script
+
+```bash
+python plapt_cli.py -s SEQ1 SEQ2 ... -m SMILES1 SMILES2 ... -o OUTPUT_FILE -f FORMAT
+```
+
+- `-s`: Followed by one or more sequences.
+- `-m`: Followed by one or more SMILES strings.
+- `-o`: (Optional) Path to the output file. If omitted, results are printed to the console.
+- `-f`: (Optional) Format of the output file (`json` or `csv`). Required if `-o` is used without specifying a file extension.
+
+#### Examples
+
+- To print results to the console:
+
+  ```bash
+  python plapt_cli.py -s SEQ1 SEQ2 -m SMILES1 SMILES2
+  ```
+
+- To save results to a JSON file:
+
+  ```bash
+  python plapt_cli.py -s SEQ1 SEQ2 -m SMILES1 SMILES2 -o results.json
+  ```
+
+- To save results to a CSV file:
+
+  ```bash
+  python plapt_cli.py -s SEQ1 SEQ2 -m SMILES1 SMILES2 -o results.csv
+  ```
+
+- To specify the format explicitly:
+
+  ```bash
+  python plapt_cli.py -s SEQ1 SEQ2 -m SMILES1 SMILES2 -o results -f json
+  ```
+
+- If `-o` is omitted, results are printed to the console.
+
+---
+
+## Using Plapt Directly in Python
+
+Apart from the command-line interface, Plapt can also be used directly in Python scripts. This allows for more flexibility and integration into larger Python projects or workflows.
+
+### Installation
+
+Ensure you have followed the installation steps mentioned in the earlier section to set up the Plapt environment and dependencies. 
+
+### Basic Usage
+
+To use Plapt in a Python script, you need to import the `Plapt` class and then create an instance of it. You can then call its methods to predict affinities.
+
+#### Importing and Initializing Plapt
+
+``` python
+# First, import the Plapt class from the package, making sure you are working in the same directory as the plapt.py file:
+from plapt import Plapt
+
+# create an instance of the Plapt class. For basic usage, no initialization parameters are needed:
+plapt = Plapt()
+```
+
+#### Running Predictions
+After initializing the `Plapt` object, you can use it to predict affinities. Here's an example of how to do it:
+
+```python
+sequences = ["APTAPSIDMYGSNNL", "PIFLNVLEAIEPGVVC"]
+smiles = ["NC(=O)[C@H](CCC(=O)O)", "NC(=[NH2+])c1ccccc1"]
+
+results = plapt.predict_affinity(sequences, smiles)
+print(results)
+```
+output:
+```
+[{'neg_log10_affinity_M': 4.38891527161495, 'affinity_uM': 40.839905489541835}, {'neg_log10_affinity_M': 4.196127195169673, 'affinity_uM': 63.66090450080189}]
+```
+The outputted json can subsequently used for other tasks.
+
+### Advanced Usage
+
+Plapt can be initialized with specialized parameters, such as the prediction module used, caching, or the inference device. Example below:
+``` python
+from plapt import Plapt
+
+# create an instance of the Plapt class with other parameters:
+plapt = Plapt(
+    prediction_module_path="models/predictionModule.onnx",  # For using a different prediction module. This is set to "models/predictionModule.onnx" by default. 
+    caching=True,  # Enable or disable caching. Enabled by default.
+    device="cuda"   # Set the computation device ("cuda" for GPU or "cpu" for CPU). If cuda isn't available on your system, it will fallback to "cpu" automatically.
+)
+```
+Each option can be specified seperately (e.g., `plapt = Plapt(caching=False)` if you would like to disable caching. 
+
+---
+
+
+#### Data Preparation and Encoding
+We source protein-ligand pairs and their corresponding affinity values from an open-source binding affinity dataset on hugginface, [binding_affinity](https://huggingface.co/datasets/jglaser/binding_affinity). We then used ProtBERT and ChemBERTa for encoding proteins and ligands respectively, giving us high quality vector-space representations. The encoding process is detailed in the `encoding.ipynb` notebook. The dataset, already encoded, is available on our [Google Drive](https://drive.google.com/drive/folders/1e-ujgHx5bW0JKxSZY5u34As77o4-IIFs?usp=sharing) for ease of access and use.
+
+#### Importing Encoders and Running the Notebook
+For users to import the encoders and run the Wolfram notebook (`WL Notebooks/FinalEssay.nb`), we provide the `encoders_to_onnx.ipynb` notebook. This ensures that users can replicate our encoding process and utilize the full capabilities of PLAPT.