Background: Calculation of ligand-binding affinity is an open problem in computational
medicinal chemistry. The ability to computationally predict affinities has a beneficial
impact in the early stages of drug development, since it allows a mathematical model to
assess protein-ligand interactions. Due to the availability of structural and binding information,
machine learning methods have been applied to generate scoring functions with good
Objective: Our goal here is to review recent developments in the application of machine learning
methods to predict ligand-binding affinity.
Method: We focus our review on the application of computational methods to predict binding
affinity for protein targets. In addition, we also describe the major available databases for experimental
binding constants and protein structures. Furthermore, we explain the most successful
methods to evaluate the predictive power of scoring functions.
Results: Association of structural information with ligand-binding affinity makes it possible
to generate scoring functions targeted to a specific biological system. Through regression
analysis, this data can be used as a base to generate mathematical models to predict ligandbinding
affinities, such as inhibition constant, dissociation constant and binding energy.
Conclusion: Experimental biophysical techniques were able to determine the structures of
over 120,000 macromolecules. Considering also the evolution of binding affinity information,
we may say that we have a promising scenario for development of scoring functions, making
use of machine learning techniques. Recent developments in this area indicate that building
scoring functions targeted to the biological systems of interest shows superior predictive performance,
when compared with other approaches.