Scoring function of protein-ligand interactions is used to recognize the “native” binding pose of a ligand on the protein and to predict the binding affinity, so that the active small molecules can be discriminated from the non-active ones. Scoring function is widely used in computationally molecular docking and structure-based drug discovery. The development and improvement of scoring functions have broad implications in pharmaceutical industry and academic research. During the past three decades, much progress have been made in methodology and accuracy for scoring functions, and many successful cases have be witnessed in virtual database screening. In this chapter, the authors introduced the basic types of scoring functions and their derivations, the commonly-used evaluation methods and benchmarks, as well as the underlying challenges and current solutions. Finally, the authors discussed the promising directions to improve and develop scoring functions for future molecular docking-based drug discovery.
TopIntroduction
The experiment-based discovery and development of novel drugs are highly complex, time-consuming and expensive (Dickson, 2004). As the number of determined three-dimensional protein structures grows and the power of high-performance computing improves, structure-based drug design and discovery is gaining prominence to identify lead compounds. This field has experienced a rapid growth in academia and pharmaceutical industry. Molecular docking is the most popular computational method employed in structure-based drug design and discovery (Kitchen, 2004; Meng, 2011; Bello, 2013).
Molecular docking mimics the recognition process in which a small molecule (ligand) translates, rotates, and twists thoroughly in the active site of a macromolecule (normally protein), with the goal of finding the most favorable binding mode measured by a scoring function of the binding interactions. Thus, the protein-ligand docking method can be described as a combination of a search algorithm and a scoring function. The search algorithm generates a number of possible poses that fit the ligand into the binding pocket of the protein. The scoring function scores each pose and then ranks the different poses that are generated by the search algorithm.
In the past three decades, multiple search algorithms were employed in the molecular docking and different types of scoring functions were developed (Dias, 2008; Guo, 2014; Liu, 2015). Various docking programs based on different search algorithms and scoring functions were developed to perform molecular docking studies and applications in structure-based drug design and discovery (Plewczynski, 2011; Sousa, 2013). There have been a mounting number of successful stories with docking-based virtual screening (Tuccinardi, 2009; Villoutreix, 2009; Danishuddin, 2015). Those successes are the outcome of the concerted applications from sampling algorithms, scoring functions as well as filtering criteria. In this chapter, the authors would like to focus on the introduction of scoring functions.