Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design.

Protein function modulation using small-molecule binding is an important therapeutic strategy for many diseases. However, many proteins remain undruggable due to the lack of suitable binding pockets for small-molecule binding. Proximity-induced protein degradation using molecular glues has recently been identified as an important strategy to target undruggable proteins.

A Bayesian walker coupled with a computational workflow that generates the micro-evolution of SARS-CoV-2 and makes predictions of new mutations that can emerge.

In this work, a Bayesian walker was constructed that generates mutations that are more prone as per UNIPROT variant data. The Bayesian walker was used to search the mutational landscape of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and a computational workflow was followed to evaluate whether a particular mutation would satisfy natural selection's fitness criteria. For SARS-CoV-2, the empirical known fitness criteria derived from SARS-CoV-2 micro-evolution data is 3-fold criteria.

Speculative assessment, molecular composition, PDOS, topology exploration (ELF, LOL, RDG), ligand-protein interactions, on 5-bromo-3-nitropyridine-2-carbonitrile.

Computational calculations of 5-bromo-3-nitropyridine-2-carbonitrile (5B3N2C) on molecular structure and on energy are implemented using the 6-311++G(d,p) basis set by DFT/B3LYP method. The UV-Vis spectrum of 5B3N2C was obtained by TD-DFT with chloroform as a solvent. The analysis of molecular electrostatic potential (MEP) and frontier molecular orbital (FMO) were used to evaluate, the entire electron density and organic reactive sites of 5B3N2C.

Target2DeNovoDrug: a novel programmatic tool for -deep learning based 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 and deep learning based approaches for 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.

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 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 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.

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.

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.