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Manuscript TF C2DeNovoDwithPropMax.pdf (858.51 kB)

Compound2DeNovoDrugPropMax –a novel programmatic tool incorporating deep learning and in silico methods for automated bio-activity discovery for any compound of interest

revised on 07.02.2021, 06:32 and posted on 08.02.2021, 11:38 by Ben Geoffrey A S, Rafal Madaj, Akhil Sanker, Pavan Preetham Valluri, Harshmeet Singh
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 have 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 presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. 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 interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization with a deep learning based
drug likeness optimization protocol. 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 program is hosted, supported and maintained at the following GitHub repository


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Declaration of Conflict of Interest

No conflict of interest to disclose