A program to automate the discovery of drugs for West Nile and Dengue virus-programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated in silico modelling.

Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning-based AutoQSAR algorithm to generate drug leads for the flaviviruses-Dengue and West Nile. The drug leads generated by the program are fed as programmatic inputs to AutoDock Vina package for automated in silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning-based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated, each time the program is run, are reflective of the constantly growing PubChem database which is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue viruses. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus was modelled in an automated way through the tool. The results are stored in the working folder of the user. Thus, our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning-based AutoQSAR algorithm and an automated in silico modelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus. The program is hosted, maintained and supported at the GitHub repository link given belowCommunicated by Ramaswamy H. Sarma.

Target2DeNovoDrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest.

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrugCommunicated by Ramaswamy H. Sarma.

Computational evaluation of the reactivity and pharmaceutical potential of an organic amine: A DFT, molecular dynamics simulations and molecular docking approach.

2-[N-(carboxymethyl)anilino] acetic acid (PIDAA) molecule has been spectroscopically characterized and computationally investigated for its fundamental reactive properties by a combination of density functional theory (DFT) calculations, molecular dynamics (MD) simulations and molecular docking procedure. A comparison drawn between the simulated and experimentally attained spectra by FT-Raman and FT-IR showed concurrence. The natural bond orbital (NBO) analysis enabled in comprehending the stability and charge delocalization in the title molecule. The first hyperpolarizability which is an important parameter for future studies of nonlinear optics (NLO) was calculated to check the potential of the molecule to be an NLO material. Besides, frontier molecular orbitals (FMO), electron localization function (ELF) and localized orbital locator (LOL) analysis were performed. Energy gap (ΔE), electronegativity (χ), chemical potential (µ), global hardness (η), softness (S), Mulliken population analysis on atomic charges and thermodynamic properties of the title compound at different temperatures have been calculated. The local reactive properties of PIDAA have been addressed by MEP and ALIE surfaces, together with bond dissociation energy for hydrogen abstraction (H-BDE). MD simulations have been used in order to identify atoms with pronounced interactions with water molecules. The pharmaceutical potential of PIDAA has been considered by the analysis of drug likeness parameters and molecular docking procedure. The biological activity of the molecule in terms of molecular docking has been analyzed theoretically for the treatment of SARS and minimum binding energy calculated. The Ramachandran plot was used to check the stereochemistry of the protein structure. In addition, a comparison of the physiochemical parameters of PIDAA and commercially available drugs (Yu et al., 2004; Tan et al., 2004; Elshabrawy et al., 2014; Chu et al., 2004; Gopal Samy and Xavier, 2015) were carried out.

Molecular docking, spectroscopic studies on 4-[2-(Dipropylamino) ethyl]-1,3-dihydro-2H-indol-2-one and QSAR study of a group of dopamine agonists by density functional method.

Density functional theory is one of the most popular accepted computational quantum mechanical techniques used in the analysis of molecular structure and vibrational spectra. Experimental and theoretical investigations of the molecular structure, electronic and vibrational characteristics of 4-[2-(Dipropylamino) ethyl]-1,3-dihydro-2H-indol-2-one are presented in this work. The title compound was characterized using FT-IR, FT-Raman and UV-Vis spectroscopic techniques. The results were compared with the theoretical calculations obtained using DFT/B3LYP with 6-311++G(d,p) as basis sets and was found to be in good agreement. The complete optimization of the molecular geometry of the title compound was carried out. Further, the vibrational assignments and calculation of potential energy distribution (PED) were reported. NLO has emerged as a key factor in recent researches. Materials showing nonlinear optical properties form the basis of nonlinear optics and development of such materials plays an important role in the present scenario. The current work provides sufficient justification for the title compound to be selected as a good non-linear optical (NLO) candidate. The electronic properties were reported using TD-DFT approach. The HOMO (EHOMO = -5.96 eV), LUMO (ELUMO = -0.80 eV) energies, energy gap and electrophilicity (2.22) was calculated in order to understand the stability, reactivity and bioactivity of the compound under investigation. To comprehend the bonding interactions we have performed the total (TDOS), partial (PDOS) and overlap population or COOP (Crystal Orbital Overlap Population) density of states. The drug likeness values were analyzed to evaluate the potential of the title compound to be an active pharmaceutical component. As a positive proof the paper further explains the molecular docking studies of the said compound. In addition, the stereochemistry of the protein structure was checked using Ramachandran plot. The title compound is a directly acting dopamine D2 agonist. In order to establish relationship between molecular descriptors of compound and its biological activity, QSAR studies have been done within the framework of DFT for 10 dopamine agonist including the title compound. Hence, the research exploration provides requisite information pertaining to the geometry, stability, reactivity and bioactivity of the compound through spectroscopic and quantum chemical methods.