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Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

preprint
submitted on 05.10.2020 and posted on 06.10.2020 by Ben Geoffrey A S, Pavan Preetham Valluri, Akhil Sanker, Rafal Madaj, Host Antony Davidd, Beutline Malgija, Konka Dinesh, Suyash Pant, Shweta Chakrabarti, Sharvani Togata, Bharti Mittal, Manish Upadhyay, Judith Gracia, Adarsh VK, Varun T K

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated In Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository

https://github.com/bengeof/Compound2Drug

History

Email Address of Submitting Author

bengeof@gmail.com

Institution

University of Madras

Country

India

ORCID For Submitting Author

0000-0001-6545-6314

Declaration of Conflict of Interest

No conflict of interest to disclose

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