The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scori...

Interactions between a protein and a ligand are often accompanied by a redistribution of the population of thermally accessible conformations. This dynamic response of the protein's functional energy landscape enables a protein to modulate binding affinities and control binding sensitivity to ligand concentration. In this paper, we investigate the structural origins of binding affinity and allo...

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this wor...

Protein receptor rearrangements upon ligand binding are a major complicating factor in structure-based drug design. An accurate prediction of these so-called induced fit phenomena calls for ligand docking and virtual screening approaches capable of considering receptor flexibility. We present Fleksy [1], a flexible approach aimed at accurately positioning small molecule ligands into a protein r...

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ...