Background: Analysis of atomic coordinates of protein-ligand complexes can provide
three-dimensional data to generate computational models to evaluate binding affinity and
thermodynamic state functions. Application of machine learning techniques can create models
to assess protein-ligand potential energy and binding affinity. These methods show superior
predictive performance when compared with classical scoring functions available in docking
Objective: Our purpose here is to review the development and application of the program
SAnDReS. We describe the creation of machine learning models to assess the binding affinity
of protein-ligand complexes.
Methods: SAnDReS implements machine learning methods available in the scikit-learn library.
This program is available for download at https://github.com/azevedolab/sandres.
SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted
Results: Recent applications of the program SAnDReS to drug targets such as Coagulation
factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring
functions to predict inhibition of these proteins. These targeted models outperform classical
Conclusion: Here, we reviewed the development of machine learning scoring functions to
predict binding affinity through the application of the program SAnDReS. Our studies show
the superior predictive performance of the SAnDReS-developed models when compared with
classical scoring functions available in the programs such as AutoDock4, Molegro Virtual
Docker and AutoDock Vina.