Drug-Target Binding Affinity Prediction

This project aims to predict the binding affinity between drugs and their target proteins using machine learning techniques. The goal is to develop robust predictive models that can aid in the drug discovery process by accurately estimating the interaction strength between drug candidates and biological targets.

Key Features:
- Data Collection and Preprocessing: Collected and preprocessed datasets containing information on drug molecules and their binding affinities to various target proteins. This step involved handling missing values, normalizing the data, and transforming categorical variables into numerical formats.
- Feature Engineering: Extracted relevant features from the drug and target protein data. This included molecular descriptors for drugs and sequence-based features for proteins.
- Model Selection and Training: Evaluated multiple machine learning algorithms, including linear regression, random forest, and gradient boosting, to identify the best-performing model for predicting binding affinities. Trained the models using a training dataset and validated their performance using cross-validation techniques.
- Hyperparameter Tuning: Optimized the model's hyperparameters using grid search and random search techniques to enhance the predictive accuracy.
- Model Evaluation: Assessed the model's performance using various evaluation metrics, such as mean squared error (MSE), R-squared, and Pearson correlation coefficient, to ensure robustness and reliability.
- Visualization: Created visualizations to represent the data distribution, feature importance, and model performance. These visualizations helped in interpreting the model's predictions and understanding the key factors influencing binding affinity.