• Munteanu, Cristian Robert
• Pimienta, Antonio Cesar
• Fernandez-Lozano, Carlos
• Melo, André
• Dias Soeiro Cordeiro, Maria Natalia
• Sousa Moreira, Irina

• Chemistry, Medicinal - Q1 - - 6/58
• Chemistry, Multidisciplinary - Q1 - - 30/148
• Computer Science, Information Systems - Q1 - - 6/135
• Computer Science, Interdisciplinary Applications - Q1 - - 7/102

Due to the importance of Hot-Spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper is presenting new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on Solvent Accessible Surface Area (SASA) and genetic conservation features subjected to simple Bayes Networks (protein-protein systems) and a more complex Multi-Objective Genetic Algorithm-Support Vector Machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools at http://bio-aims.udc.es/MolStructPred.php and are available for download.