Note: We are currently updating Taba to integrate it with SAnDReS 2.0 (de Azevedo et al., 2024). Taba 2.0 will employ DOME statistics (Walsh et al., 2021) to evaluate machine-learning models and explore the scoring function space concept with additional regression methods available in Scikit-Learn library (Pedregosa et al., 2011). We expect to release a new version of Taba in 2025. Prof. Walter F. de Azevedo, Jr. (Posted on October 10, 2024)
Please cite the following reference (da Silva AD et al., 2020) if the Taba program was useful.
da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69-73. doi: 10.1002/jcc.26048. PubMed
Taba is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. You can use a higher version as well.
You need to have Python 3 installed on your computer to run Taba. In addition, you also need NumPy (1.14.5*), Matplotlib, scikit-learn (0.19.1*), pyqt4 and SciPy (1.1.0*).
*You can use higher versions as well.
Step 1. Download Taba (available here)
Step 2. Unzip the zipped file (TABA_dist)
Step 3. Copy TABA_dist directory to c:\
Step 4. Open a command prompt window (Terminal) and type: cd c:\TABA_dist
then type: python taba.py
This launches GUI window for Taba. That´s it, good Taba session. See help for additional information about how to run Taba.
Step 1. Download Taba (available here)
Step 2. Unzip the zipped file (TABA_dist)
Step 3. Copy TABA_dist directory to the directory of your choice
Step 4. Open a terminal and type cd /your personal directory/TABA_dist
then type: python taba.py
This launches GUI window for Taba. That´s it, good Taba session. See help for additional information about how to run Taba.
Taba was developed by Amauri Duarte and Dr. Walter F. Azevedo Jr. (walter@azevedolab.net)
Last update of the code on December 04, 2019.