Current Drug Targets

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

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Current Medicinal Chemistry

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

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Combinatorial Chemistry and High Throughput Screening

Section Editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry and High Throughput Screening ISSN: 1875-5402

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Current Bioinformatics

Member of the Editorial Board for the Current Bioinformatics ISSN: 2212-392X (Online) ISSN: 1574-8936 (Print)

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Organic and Medicinal Chemistry International Journal

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

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Bioengineering International

Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119

Bioengineering International



Message posted on September 11, 2021.

Dear SAnDReS Users,

Our program went through an extensive upgrade, and now it has a fully integrated interface that allows developing targeted scoring functions using Scikit-Learn. SAnDReS is running smoothly on Linux and will be available for downloading soon. 

Thank you very much for your patience.  

Stay safe,

                                    Dr. Walter F. de Azevedo, Jr.
                                    I am a freethinker influencing science and technology.


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 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 80 citations. We expect to release SAnDReS 2.0 in October/2021.


  • MVD (Windows)   MVD (Linux)
  • SAnDReS 2.0.0 (Windows) (Under development). Only download, make datasets, and docking simulations with AutoDock Vina are working. It is not a stable version. I am working on the updating of machine learning part of the code. Last updated on May 30, 2021.                                                              Dr. Walter F. de Azevedo, Jr.    


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. doi: 10.2174/0929867328666210210121320.   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