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Editorships

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

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

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Member of the Editorial Board for the PeerJ ISSN: 2167-8359

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

 

Research

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Protein-Ligand Interacions

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Molecular Docking

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Bioinspired Computing

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Computational Systems Biology

 

Recent Publications

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Volkart PA et al. Curr Drug Targets. 2019;20(7):716-726

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Russo S, De Azevedo WF. Curr Med Chem 2019. 26(10):1908-1919

SAnDReS    

      

SAnDReS draws inspiration from several protein-ligand projects 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 and inhibitors (De Azevedo et al., 1996; 1997). SAnDReS is a free and open-source (GNU General Public License) computational environment for the development of machine-learning models for prediction of ligand-binding affinity. SAnDReS is also a tool for statistical analysis of docking simulations and evaluation of the predictive performance of computational models developed to calculate binding affinity. We have implemented machine learning techniques to generate regression models based on experimental binding affinity and scoring functions such as PLANTS and MolDock scores. The scikit-learn library has a wide spectrum of supervised machine learning techniques for regression, such as Ordinary Least Squares and Ridge Regression. SAnDReS was developed using Python programming language, and SciPy, NumPy, scikit-learn, and Matplotlib libraries. Data obtained from any protein-ligand docking program can be analyzed by SAnDReS, the only requisite is to have protein structures in Protein Data Bank (PDB) format, ligands in Structure Data File (SDF) format, docking and scoring function data in comma-separated values (CSV) format. This program has been applied to several datasets comprised of crystallographic structures for which there is information for the ligand-binding affinity, in order to generate scoring functions tailored to the biological system of interest (Xavier et al., 2016).  

Gallery of Plots Generated by the Program SAnDReS  

 

   

Scatter plots and ROC Curve generated by SAnDReS  

The flowchart below illustrates the main steps to integrate a molecular docking program and SAnDReS.


Flowchart for application of SAnDReS to analyze docking results and develop scoring functions. Grey boxes indicate tasks carried out by SAnDReS.

SAnDReS

 Installation  

Start thinking outside the box for docking simulations. Use SAnDReS, it is reliable, fast, easy, free, and funny. 

Download from GitHub  

You may download SAnDReS code from GitHub.

Installing SAnDReS without Installers (Windows)  

You need to have Python 3 installed on your computer to run SAnDReS. Also, you need to install NumPyMatplotlibscikit-learn, and SciPy.  

You can make the installation process easier by installing Anaconda. 

Step 1. Install Anaconda 32 bits (download here)  

Step 2. Download SAnDReS 1.1.0 from GitHub (here)    

Step 3. Unzip the zipped file (sandres.zip) 

Step 4. Copy sandres directory to c:\ .

Step 5. Open a command prompt window and type: cd c:\sandres

then type: python sandres1_GUI.py

This launches GUI window for SAnDReS. That´s it, good SAnDReS session. You can also start SAnDReS clicking on the sandres.bat file. You may also create a shortcut for SAnDReS right clicking on the sandres.bat file. 

SAnDReS 1.1.0 GUI window.

Download Installers   

We provide here the SAnDReS installers for Linux and Windows. These installers were developed by Mr. Amauri Duarte da Silva. You don't need to have Python installed on your computer to run it. Just go through the installation instructions below, and enjoy using SAnDReS, a new way to think about protein-ligand docking. Last updated on May 19, 2018.

    

Installing SAnDReS (Windows)   

You need administrator privileges to install SAnDReS. The easiest way to install SAnDReS (Windows) on your computer is via the stand-alone installers, which you can download from the above links. To install SAnDReS, unzip the zip file and click on the installer file. Keep the installation folder indicated by the installer (c:\sandres). Once finished the installation, you will have a desktop icon for SAnDReS, just click on it and start your SAnDReS session. We have tested this installer (Windows 64 bits) on computers running Windows 8.1. It worked fine with us. If you have  any question regarding the installation process, please feel free contact me by e-mail: walter@azevedolab.net  .

Installing SAnDReS (Linux) 

To install SAnDReS (Linux) on your computer use the stand-alone installers, which you can download from the above links. To install SAnDReS, unzip the tar.gz file and then you can run SAnDReS.  

Biological Systems Analyzed by SAnDReS

Below you have a list of biological systems (datasets) that were analyzed using SAnDReS. Each zipped folder has the necessary files to reproduce the results reported for each dataset. 

     -Coagulation Factor Xa with Ki Information   ZIP   PubMed   
     -Cyclin-Dependent Kinases with IC50 Information   ZIP   PubMed   
     -Cyclin-Dependent Kinases with Ki Information   ZIP   (to be published)   
     -High-resolution Structures with Delta G Information   ZIP   Link     
     -DeltaG   ZIP   
     -High-resolution Structures with IC50 Information   ZIP   PubMed     
     -HIV-1 Protease with Ki Information   ZIP   PubMed   

Related Links  

     -A Database of Useful Decoys: Enhanced (DUDE)     
     -Enzyme Nomenclature Database (Expasy)     
     -Scikit-learn Machine Learning Techniques for Regression   
     -Matplotlib     
     -NumPy     
     -Protein Data Bank (PDB)     
     -Python      
     -SAnDReS       
     -SciPy      
     -UCI Machine Learning Repository   
     -Wolfram Demonstration Projects for Machine Learning   
     -Wolfram Demonstration Projects for Regression     
     -Wolfram Demonstration Projects for Stochastic Gradient Descent