Innovative Techniques for Enhancing the Reliability of Machine Learning Classifiers in Protein-Protein Interaction Hotspot Prediction

Abstract

Protein-protein interactions (PPIs) play a crucial role in numerous biological processes, with specific regions known as hotspots being key determinants of binding affinity and stability. Accurate prediction of these interaction hotspots is essential for understanding molecular mechanisms and facilitating drug discovery. Machine learning (ML) classifiers have emerged as powerful tools for PPI hotspot prediction due to their ability to identify complex patterns in large biological datasets. However, challenges such as data imbalance, model overfitting, and limited generalizability often affect the reliability of these classifiers Consequently, the objective of this review is to explore innovative techniques that enhance the reliability of Machine learning (ML) classifiers for Protein-protein interactions (PPI) hotspot prediction using multi-omics data, explainable AI (XAI) and transfer learning to improve model performance and interpretability. Key approaches include advanced feature engineering, integration of multi-omics data, ensemble learning methods, and the application of deep learning architectures. Additionally, strategies for addressing data-related issues, such as synthetic data generation and transfer learning, are discussed. The review also highlights the importance of model interpretability and robust validation techniques to improve predictive performance. By examining these cutting-edge methodologies, this paper provides insights into the development of more accurate and reliable ML models, ultimately contributing to advancements in computational biology and therapeutic target identification.