Books

 

 

 

Projects

 

 

 

Citation

 

Editorships

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

Combinatorial Chemistry and High Throughput Screening

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

Bentham Link

Current Bioinformatics

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

Bentham 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

Bioengineering International

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

Bioengineering International

 

SAnDReS 2.0

You need Python 3 installed on your computer to run SAnDReS 2.0. In addition, you also need MatplotlibNumPyScikit-LearnSciPy, and XGBoost. It is also necessary to have MGLTools 1.5.6 installed on your computer. You can make the installation of Python packages faster by installing Anaconda

Installing on Linux

Step 1. Install Anaconda (available here: https://www.anaconda.com/download/)

Step 2. Install MGLTools 1.5.6 (available here)

Step 3. Install XGBoost (available here: https://xgboost.readthedocs.io/en/latest/install.html#python)

Step 4. Download SAnDReS 2.0 (available here: sandres2.zip). Copy the sandres2 zipped directory (sandres2.zip) to wherever you want it and unzip the zipped directory. Type the following command: unzip sandres2.zip

Step 5. Open a terminal and cd to sandres2 directory then, type python run_program.py 

Now we have the GUI window for SAnDReS 2.0. That´s it, good SAnDReS session. By December 2021, we will have a tutorial page for additional information about how to run SAnDReS.

Installing on Windows

Step 1. Install Anaconda (available here: https://www.anaconda.com/download/)

Step 2. Install MGLTools 1.5.6 (available here)

Step 3. Install XGBoost (available here: https://xgboost.readthedocs.io/en/latest/install.html#python)

Step 4. Download SAnDReS 2.0 for Windows (available here: sandres_win.zip). Copy the sandres2_win zipped directory (sandres_win.zip) to wherever you want it and unzip the zipped directory. 

Step 5. Open a command prompt and cd to sandres2_win directory then, type python run_program.py 

Now we have the GUI window for SAnDReS 2.0. That´s it, good SAnDReS session. By December 2021, we will have a tutorial page for additional information about how to run SAnDReS. This version has been tested on Windows 10. 

*SAnDReS 2.0 uses AutoDock Vina 1.1.2 as docking engine. In November 2021, Scripps Institute released AutoDock Vina 1.2. Future developments of SAnDReS will integrate the latest version of AutoDock Vina.

Overview 

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 2.7.11.22) 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 90 citations

References    

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