Current Drug Targets

Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592

Bentham Link

Current Medicinal Chemistry

Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X

Bentham Link

Journal of Molecular Structure

Member of the Editorial Board of the Journal of Molecular Structure ISSN: 0022-2860

Journal of Molecular Structure Link

Molecular Diversity

Member of the Editorial Board of Molecular Diversity ISSN: 1381-1991 (Print) 1573-501X (Online)

Molecular Diversity Link

Exploration of Drug Science

Associate Editor for Exploration of Drug Science

Exploration of Drug Science Link

Frontiers in Chemistry

Reviewer Editor for Frontiers in Chemistry ISSN: 2296-2646

Frontiers in Chemistry Link

Organic and Medicinal Chemistry International Journal

Member of the Editorial Board for the Organic and Medicinal Chemistry International Journal ISSN: 2474-7610

Bentham Link




SAnDReS 2.0 brings together the most advanced tools for protein-ligand docking simulation and machine-learning modeling. We have the newest version of AutoDock Vina, available in July 2022 (version 1.2.3), as a docking engine. Also, SAnDReS 2.0 uses the latest version of Scikit-Learn, available in July 2022 (version 1.1.1). It has 64 regression methods which allow us to explore the Scoring Function Space (SFS). This exploration of the SFS permits us to have an adequate machine-learning (ML) model for a targeted protein system. SAnDReS predicts binding affinity for a specific protein system with superior performance compared against classical scoring functions. In summary, SAnDReS 2.0 makes it possible for you to design a scoring function adequate to the protein system of your interest. 


SAnDReS (Statistical Analysis of Docking Results and Scoring functions) draws inspiration from several protein systems that we have been working on in the last decades. These projects began in the 1990s with pioneering studies focused on intermolecular interactions between cyclin-dependent kinase (CDK) (EC and inhibitors (de Azevedo et al., 1996de Azevedo et al., 1997). SAnDReS is a free and open-source (GNU General Public License) computational environment for the development of machine-learning models (Bitencourt-Ferreira & de Azevedo, 2019; Bitencourt-Ferreira et al., 2021; Bitencourt-Ferreira, Rizzotto et al., 2021) for the prediction of ligand-binding affinity (Xavier et al., 2016; Bitencourt-Ferreira & de Azevedo, 2019; Veit-Acosta & de Azevedo, 2021). We developed SAnDReS using Python programming language, and Pandas,  SciPyNumPyscikit-learn (Pedregosa et al., 2011), and Matplotlib libraries as a computational tool to explore the scoring function space (Heck et al., 2017; Bitencourt-Ferreira & de Azevedo, 2019). SAnDReS 1.0 has been applied to several protein systems and has over 90 citations


Bitencourt-Ferreira G, de Azevedo WF Jr. SAnDReS: A Computational Tool for Docking. Methods Mol Biol. 2019; 2053: 51–65.   PubMed   

Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019; 2053: 251–273.   PubMed   

Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019; 2053: 275–281.   PubMed   

Bitencourt-Ferreira G, da Silva AD, de Azevedo WF Jr. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets. A Study of Cyclin-Dependent Kinase 2. Curr Med Chem. 2021; 28(2): 253–265.   PubMed   

Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions. Development and Applications With SAnDReS. Curr Med Chem. 2021; 28(9): 1746–1756.   PubMed   

De Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A. 1996; 93(7): 2735–2740.   PubMed   

De Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH. Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem. 1997; 243(1-2): 518–526.   PubMed   

Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459–2470.   PubMed   

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Verplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011; 12: 2825–2830.   PDF    

Veit-Acosta M, de Azevedo Junior WF. The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity. Curr Med Chem. 2021; 28(34): 7006–7022.   PubMed   

Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801–812.   PubMed