1. Computational intelligence methods in protein-ligand interactions 2. Random forest method for predicting protein ligand-binding residues 3. Encoders of protein residues for identifying protein-protein interacting residues 4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods 5. Semi-supervised prediction of protein interaction sites from unlabeled sample information 6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm 7. Evolutional algorithms and their applications in protein long-range contact prediction 8. A novel robust geometric approach for modelling protein-protein interaction networks 9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis 10. Ensemble learning-based prediction on drug-target interactions 11. Convolutional neural networks for drug-target interaction prediction 12. Ensemble learning methods for drug-induced liver injury identification 13. Database construction for mutant protein interactions 14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy

Presents a guide to computational techniques for protein-ligand interaction analysis Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem Identifies appropriate computational tools for various problems Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning Offers the computational, mathematical and statistical skills researchers need