Antibody-based therapeutics play a crucial role in treating cancers and autoimmune diseases. A key factor in their success is their ability to internalize into target cells, which influences delivery efficacy, especially in antibody-conjugates (ACs). Machine learning (ML) offers an approach to predicting antibody internalization efficiency based on molecular and cellular features. This paper explores ML applications in this area, focusing on data preprocessing, model selection, feature engineering, and evaluation metrics.

ML models require high-quality data for accurate predictions. Data sources for antibody internalization prediction include :

• Experimental datasets: High-throughput screening (HTS) data, flow cytometry measurements, and live-cell imaging results.
• Computational datasets: Structural and physicochemical properties of antibodies extracted from databases such as Protein Data Bank (PDB) and UniProt.
• Omics datasets: Transcriptomics and proteomics data providing insights into receptor expression levels on target cells.
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Data Preprocessing Steps :

1. Cleaning: Removing missing values and erroneous entries.
2. Normalization: Scaling features such as molecular weight, hydrophobicity, and charge for uniformity.
3. Feature Selection: Selecting key attributes such as receptor binding affinity, endocytosis rate, and surface expression levels.
4. Dimensionality Reduction: Techniques such as Principal Component Analysis (PCA) or Autoencoders reduce redundant information.

Feature engineering enhances model performance. Essential features for predicting antibody internalization include :

• Biochemical Properties : Charge distribution, pKa values, and isoelectric points.
• Structural Features : Epitope-paratope interactions, secondary structure motifs, and solvent accessibility.
• Receptor Dynamics : Receptor turnover rate, endosomal escape efficiency, and lysosomal degradation probability.
• Cellular Context : Expression levels of Fc receptors and associated signaling pathways.

Feature selection methods such as Recursive Feature Elimination (RFE) and LASSO regression identify the most relevant variables.

Several ML models can predict antibody internalization efficiency :

1. Supervised Learning Models

• Random Forest (RF) : Handles non-linearity and feature interactions.
• Support Vector Machines (SVM) : Useful for high-dimensional datasets with complex decision boundaries.
• Gradient Boosting Machines (GBM) : Includes XGBoost, LightGBM, and CatBoost, providing efficient feature importance rankings. Learn more
• Artificial Neural Networks (ANNs): Capture complex patterns in high-dimensional spaces. Learn more

2. Unsupervised Learning Models

• Clustering Algorithms : k-Means and Hierarchical Clustering categorize antibodies based on internalization profiles.
• Autoencoders : Extract latent representations from high-dimensional features.

3. Hybrid Models

Combining supervised and unsupervised approaches can improve predictions. For instance, unsupervised feature extraction followed by supervised learning enhances model performance.

Once the dataset is preprocessed and features are selected, models are trained. Common evaluation metrics include :

• Accuracy: Measures overall correctness of predictions.
• Precision and Recall: Important for imbalanced datasets where false negatives have severe consequences.
• F1 Score: Balances precision and recall, providing a comprehensive evaluation metric.
• ROC-AUC (Receiver Operating Characteristic - Area Under Curve): Measures the ability of the model to distinguish between internalizing and non-internalizing antibodies.

Hyperparameter tuning methods such as Grid Search and Bayesian Optimization improve model performance.

Explainability and Interpretability

Understanding model predictions is crucial for biological insights. Methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) interpret model outputs and determine the most influential features in antibody internalization.

Predicting antibody internalization efficiency using ML has broad applications :

1. Therapeutic Development: Enhancing the design of ACs for targeted therapies.
2. Personalized Medicine: Tailoring antibody treatments based on patient-specific receptor expression profiles.
3. High-Throughput Screening: Reducing experimental workload by prioritizing promising candidates computationally.

Future research directions include :

• Integration with Multi-Omics Data: Combining genomics, proteomics, and metabolomics for better predictive accuracy.
• Reinforcement Learning: Adapting RL techniques to optimize antibody designs.
• Federated Learning: Training ML models on decentralized datasets while preserving data privacy.