QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and in silico methods for automated in silico bio-activity discovery for any compound of interest.

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 protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between 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. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating 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. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax.HIGHLIGHTSDeep learning based network pharmacology approach to predict the bio-activity of compounds.Further optimization of the compound toward drug like properties using deep learning techniques.Automated in silico modeling and interaction profiling of deep learning predicted target protein-ligand interaction.Communicated by Ramaswamy H. Sarma.

Spectroscopic, chemical reactivity, molecular docking investigation and QSAR analyses of (2E)­1­(3­bromo­2­thienyl)­3­(2,5­dimethoxyphenyl)prop­2­en­1­one.

Chalcone derivative of (2E)­1­(3­bromo­2­thienyl)­3­(2,5­dimethoxyphenyl) prop­2­en­1­one (BTD) molecule has been deliberated for spectroscopic properties experimentally and theoretically. The title compound was characterized by FT-IR, FT-Raman and UV-Vis analyses. The structural activity and vibrational wavenumbers were calculated by a DFT method. The Natural Bond Orbital (NBO) analysis which reveals the hyper conjugative interactions of the present molecule has been performed. Meanwhile, the Chemical reactivity of Condensed Fukui function, MEP and HOMO-LUMO energies of the molecule were also analyzed. Furthermore, Multiwfn 3.3.9 program has been utilized to study MEP and the electron excitation analysis. Docking studies which play a significant role in determining the endothelial nitric oxide synthase inhibition activity of the present compound have also been carried out to predict the binding energy and inhibition constant of the title compound. In addition, drug resemblance parameters have also considered by QSAR study in which the comparison of chemical parameters of chalcone drugs of title molecule has been done.