ABacAPP is a machine learning-based web application capable of predicting the antibacterial potency of any compound against β-lactamases (including β-lactamase, AmpC, Bla2, KPC-2, TEM-1, BRO-1, Class C, Class D, Type II, L1, NDM-1, SHV-1, SHV-5, OXA-1, and Metallo β-lactamase 1 and 2), DNA Gyrases (including DNA Gyrase, GyraseA, and GyraseB), Bacterial Dihydrofolate Reductases, and Penicillin-Binding Proteins (including 1B, 2, 2B, 2X, MecA, and D-alanyl-D-alanine carboxypeptidase).
Naeem Abdul Ghafoor¹² & Omur Baysal2
²Mugla Sitki Kocman University, Faculty of Science, Department of Molecular Biology and Genetics, Mugla, Turkey.
- Click on the "Open in Streamlit" badge above.
- Enter the SMILE for the compound of your interest.
- Predicted IC50 will be printed out within a few seconds.
Download the project
wget https://github.com/naeemmrz/ABacAPP.gitUnzip and enter the project directory
unzip ABacAPP-main.zip
cd ABacAPP-mainInstall dependencies
pip install -r requirements.txt #System level packages are provided in packages.txtStart the application
streamlit run MDM2pred.pyThe Application will open in your default browser with the same interface as the online version.
ABacAPP is developed and maintained by Naeem A. from YNLab at the Department of Molecular Biology and Genetics, Mugla Sitki Kocman University, Mugla, Turkey. The development of ABacAPP was funded by the The Scientific and Technological Research Institution of Turkey (TUBITAK) under the project number 122E082 and supervised by Prof. Dr. Omur Baysal from the Molecular Microbiology Laboratory at the Department of Molecular Biology and Genetics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Turkey. The numerical calculations that lead to the development of ABacAPP were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources). The authors/developers are grateful to TUBITAK and TRUBA for the funding and resources they've provided.