The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data mining in bioinformatics using Weka
Bioinformatics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Automating molecular docking with explicit receptor flexibility using scientific workflows
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
Data Mining in Bioinformatics
FReDD: Supporting Mining Strategies through a Flexible-Receptor Docking Database
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
| Hi-index | 0.00 |
Recent progress in structural biology and bioinformatics contributed to the increased amount of data that need to be stored and analyzed. Advances in data mining research have allowed the development of efficient methods to find interesting patterns in large databases. In this context, this work proposes a method to automatically extract detailed information from molecular docking experiments. Completely flexible molecular docking studies (including ligand and receptor explicit flexibilities) of the InhA enzyme from Mycobacterium tuberculosisin complex with NADH were performed with AutoDock3.05 using receptor snapshots generated by nanosecond molecular dynamics simulations. To analyze the results we applied our data mining method which was capable of identifying important information about intermolecular interactions and association rules. The method allowed a fast and concise analysis which led to identification of relevant residues and conformations essential to ligand binding.