Molecular Property Diagnostic Suite – Compound Library (MPDS-CL)
Molecular Property Diagnostic Suite-Compound Library (MPDS-CL), is an open-source galaxy-based cheminformatics web-portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds has been done and deposited in MPDS-CL. A 56-bit fingerprint-based classification algorithm which led to a formation of 56 structurally well-defined classes. This is the official open source repository for the following paper:
Lijo John, Selvaraman Nagamani, Hridoy Jyoti Mahanta, S. Vaikundamani, Nandan Kumar, Asheesh Kumar, Esther Jamir, Lipsa Priyadarsinee, G. Narahari Sastry (2023): Molecular Property Diagnostic Suite Compound Library (MPDS-CL): A Structure based Classification of the Chemical Space. https://doi.org/10.1007/s11030-023-10752-1
Requirements
• Python 3.9 & above
• RDkit - 2022.03
• Openbabel - v.3.0.0
• Install these two packages in conda environment and activate which running the script.
Usage
Required scripts are given in the scripts folder.
Users can include the list of SMILES in “dataset.txt” and run “main.py” script. The output will be saved in “fp.txt” and “final_mpds_class_ouput.txt”. The “fp.txt” contains 56 bit fingerprint for each input compound. Each bit represents the presence of 56 features or class (0 absent; 1 present) in the compound. A compound can have multiple features but will belong to only 1 class based on priority. The output contains four columns
1st column – Identified MPDS class number
2nd column – Generated 56 bit FP
3rd column – Molecular weight of the molecule
4th column - input SMILES
People
• Lijo John
• Selvaraman Nagamani
• Hridoy Jyoti Mahanta
• S. Vaikundamani
• Nandan Kumar
• Asheesh Kumar
• Esther Jamir
• Lipsa Priyadarsinee
• G. Narahari Sastry
If you find this useful in your research, please cite
John, L., Nagamani, S., Mahanta, H.J. et al. Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10752-1