These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
QC2DenovoDpropmaxfinal.pdf (864.43 kB)

QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

revised on 21.04.2021, 11:14 and posted on 22.04.2021, 08:07 by Ben Geoffrey A S, Rafal Madaj, Akhil Sanker, Pavan Preetham Valluri
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

Anticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use
the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep
learning models into a quantum layer and introduce quantum layers into classical models to produce a
quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the
same is provided below


Acknowledge the support of the computational resources of the PLGrid Infrastructure


Email Address of Submitting Author


Independent Researcher



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest to disclose