Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS.BACKGROUND
 Our purpose here is to review the development and application of the program SAnDReS.  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 scoring functions.


 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.  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.
SAnDReS
SAnDReS implements machine learning methods available in the scikit-learn library. 
scikit-learn library
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.
AutoDock4
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.
Molegro Virtual Docker
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.
AutoDock Vina