Abstract
The amount of resolved X-ray structures of protein-ligand complexes have exploded during the last decade. This has initiated much improvement of docking methods by an advanced knowledge about the key interactions in the complexes, nevertheless, it still remains a challenge even to reproduce known experimental results by ligand docking. A number of docking methods for predicting binding modes of small molecules have been developed, methods which are also thought to help to quantify energetics of different molecular interactions. Ligand docking is mainly used by the pharma industry for identifying possible compounds for development in the drug discovery process, usually in the very early hit identification phase, but also at later stages of lead optimisation. The quality of different docking methods has been thoroughly investigated, however, the relationship between methods, scoring functions and target proteins on one hand, and docking performance on the other hand still seems poorly understood. Scoring functions are especially important since minimisation algorithms rely on these functions. Therefore, an accurate scoring function is absolutely crucial to obtain correct results, i.e. correct binding modes but also correct ranking of docked ligands. The accuracy of scoring functions is target dependent, which implies that it is important to study the scope and limitations of these functions. In this report, we discuss some of the available docking methods and scoring functions applied to relevant targets for the pharma industry.
Keywords: ligand docking, scoring function, virtual screening, structure-based drug design, protein kinase, protease, neuraminidase, gpcr
12