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EDA and Deep Learning for Drug Discovery -

Overview

This notebook focuses on leveraging Deep learning/Machine Learning techniques for drug discovery, specifically predicting the binding affinities of small molecules to proteins. Using the BELKA dataset provided by Leash Biosciences, this project aims to explore a chemical space of small molecules and accelerate the identification of potential drug candidates.

The dataset contains chemical and protein information, representing molecular structure as SMILES strings. The primary goal is to predict whether small molecules bind to specific protein targets, assisting in the drug discovery process.

Problem Definition

The notebook addresses a binary classification problem, where the target variable is binds (1 for binding, 0 for non-binding). The dataset includes:

• Chemical Structures (SMILES):
  • Molecule building blocks (first, second, and third).
  • Fully assembled molecules with a triazine core and DNA linker.
• Protein Target Information:
  • Three protein targets (EPHX2, BRD4, ALB).
• Binding Label (binds):
  • A binary label indicating whether a molecule binds to a specific protein.

Main Sections

1. Data Preprocessing

• Loading and Sampling: The dataset is loaded from a Parquet file, and a sample is created for efficient exploratory analysis.
• Handling SMILES: Introduction to SMILES (Simplified Molecular Input Line Entry System), a chemical notation for representing molecular structures.

2. Exploratory Data Analysis (EDA)

• Visualizations and statistical analysis are performed on the dataset to understand patterns and distributions.
• Examines the relationship between chemical structures and their binding to proteins.

3. Modeling

• Feature Engineering: Conversion of SMILES strings into features usable by machine learning models.
• Model Training: Application of MLP algorithm to predict the binding affinities.
• Evaluation: The trained model is evaluated using metrics like accuracy, precision, and recall.

4. Results and Discussion

• Summary of findings from the model.
• Potential areas for improvement in the prediction of molecular binding.

Dataset

The dataset used in this notebook is in Parquet format and contains both chemical and protein data. The SMILES strings represent molecules, while the target proteins and the binary binding label provide the supervised learning framework.

Prerequisites

Ensure you have the following libraries installed:

• pandas
• numpy
• scikit-learn
• rdkit (for chemical informatics)
• duckdb (for data handling)

Running the Notebook

1. Load the dataset: The data is stored in a Parquet file (train.parquet) and (test.parquet) on this page Visit Kaggle. You may modify the path based on your directory structure.
2. Run each cell sequentially: The notebook is designed for step-by-step execution.
3. Model Training: After feature extraction, models like Random Forest, Gradient Boosting, or Neural Networks can be trained.

Future Work

• Enhance feature extraction techniques from SMILES strings.
• Experiment with different machine learning models to improve predictive performance.
• Explore larger datasets for better generalization.

Conclusion

This project demonstrates the potential of machine learning in the drug discovery process by predicting protein-ligand binding interactions using molecular data. It highlights the importance of feature extraction from chemical notations like SMILES and provides a strong foundation for further research in computational drug discovery.

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