🌟 Usefulness and Purpose of ML Implementation for Bioactivity Prediction of Target Proteins
🔹 Drug Discovery Acceleration – ML helps in predicting the bioactivity of compounds against a target protein, significantly reducing time and cost in drug discovery.
🔸 Feature Selection – Variance Thresholding eliminates low-variance features, ensuring the dataset retains only informative features.
🟢 Handling Large Data – ML models efficiently process large-scale bioactivity datasets, identifying patterns humans might miss.
🔵 Regression Analysis – Random Forest Regression predicts bioactivity values (pIC50), providing insights into compound effectiveness.
🟠 Statistical Validation – R-squared value helps evaluate model performance, ensuring reliability in bioactivity predictions.
🔻 Automated Workflow – Streamlit-based UI streamlines dataset selection, preprocessing, and model execution interactively.
🟣 Model Generalization – Train-test split ensures robust performance assessment, avoiding overfitting.
🔵 Compound Filtering – Removing non-informative molecular descriptors improves predictive accuracy.
🟡 Data Visualization – Regression plots (Seaborn) visually depict experimental vs. predicted pIC50 values for better interpretation.
🟠 Biological Relevance – Helps in identifying high-affinity drug candidates for specific target proteins in diseases like Alzheimer's.
🔹 Customizable Analysis – Supports different diseases and descriptor files, making the implementation flexible.
🟢 Non-Experimental Predictions – Reduces the need for extensive lab testing by providing preliminary computational insights.
🔻 Pattern Recognition – Captures nonlinear relationships between molecular descriptors and bioactivity using Random Forest.
🔸 Scalability – ML methods can be adapted to analyze multiple target proteins for different diseases.
🟣 Preprocessing Automation – Eliminates missing values and redundant features, ensuring a cleaner dataset.
🟠 Facilitates Hypothesis Testing – Helps researchers validate drug-target interaction hypotheses before experimental validation.
🟡 Cross-Disciplinary Utility – Useful for computational chemistry, pharmacology, and bioinformatics researchers.
🔵 Interactive Decision-Making – Streamlit UI allows real-time selection and visualization of results.
🔹 Reproducibility – The ML pipeline ensures standardized processing, making results consistent and comparable.
🟢 Future Applications – Can be extended to deep learning models for even more accurate bioactivity predictions.
🌟Implementation Steps:
1️⃣ The ChemBL ID, SMILES and IC50 values fetched automatically from chembl_webresource_client and molecular fingerprints from PubChem API, ensuring the data is stored in a structured format (CSV or DataFrame). The dataset should contain: Molecular Identifiers (e.g., ChemBL ID) Fingerprint Descriptors (Bit-vector/Binary representation) IC50 Values (Bioactivity measurement in nM)
💡 Converted IC50 values to pIC50 for better statistical modeling:
2️⃣ Data Preprocessing 🔹 Load the Data 🔹 Handle Missing Data 🔹 Remove Low-Variance Features
3️⃣ Train-Test Splitting 4️⃣ Train the ML Model 5️⃣ Model Evaluation 6️⃣ Predictions & Visualization 7️⃣ Interactive Streamlit App Future Enhancements:- Use Deep Learning (Neural Networks) for Bioactivity Prediction. Implement Explainable AI (SHAP values) to interpret feature importance. Automate descriptor extraction from PubChem API.