Two-photon microscopy depends extensively on the two-photon absorption cross-sections of biologically relevant chromophores. High repetition rate (HRR) lasers are essential in multiphoton microscopy for generating satisfactory signal to noise at low average powers. However, HRR lasers generate thermal distortions in samples even with the slightest single photon absorption. We use an optical chopper with HRR lasers to intermittently 'blank' irradiation and effectively minimize thermal effects to result in a femtosecond z-scan setup that precisely measures the two-photon absorption (TPA) cross-sections of chromophores. Though several experimental factors impact such TPA measurements, a systematic effort to modulate and influence TPA characteristics is yet to evolve. Here, we present the effect of several control parameters on the TPA process that are independent of chromophore characteristics for femtosecond laser pulse based measurements; and demonstrate how the femtosecond laser pulse repetition rate, chromophore environment and incident laser polarization can become effective control parameters for such nonlinear optical properties.

A series of benzotriazole derivatives (compounds ) were synthesized, and (compounds , ) of which were first reported. Their chemical structures were confirmed by means of H NMR, IR and elemental analyses, coupled with one selected single crystal structure (compound ). All the compounds were assayed for antibacterial activities against three Gram positive bacterial strains ( and ) and three Gram negative bacterial strains ( and ) by MTT method. Among the compounds tested, most of them exhibited potent antibacterial activity against the six bacterial strains. Most importantly, compound () showed the most favourable antibacterial activity against and with MIC of 1.56 µg/mL, 1.56 µg/mL, 1.56 µg/mL, 3.12 µg/mL, 6.25 µg/mL and 6.25 µg/mL, respectively.

3a is an accessory protein from SARS coronavirus that is known to play a significant role in the proliferation of the virus by forming tetrameric ion channels. Although the monomeric units are known to consist of three transmembrane (TM) domains, there are no solved structures available for the complete monomer. The present study proposes a structural model for the transmembrane region of the monomer by employing our previously tested approach, which predicts potential orientations of TM -helices by minimizing the unfavorable contact surfaces between the different TM domains. The best model structure comprising all three -helices has been subjected to MD simulations to examine its quality. The TM bundle was found to form a compact and stable structure with significant intermolecular interactions. The structural features of the proposed model of 3a account for observations from previous experimental investigations on the activity of the protein. Further analysis indicates that residues from the TM2 and TM3 domains are likely to line the pore of the ion channel, which is in good agreement with a recent experimental study. In the absence of an experimental structure for the protein, the proposed structure can serve as a useful model for inferring structure-function relationships about the protein. Graphical AbstractThe structure of the membrane protein 3a from SARS coronavirus is modeled using an approach that minimizes unfavorable contacts between transmembrane domains. A structure for a complete monomeric form of the protein thereby proposed is able to account for the behavior of the protein reported in previous experimental studies.

Kinetics of Permanganate (MnO ) oxidation of antiviral drug, valacyclovir hydrochloride (VCH) has been studied spectrophotometrically at a constant ionic strength of 0.1 mol dm. The reaction exhibiting a 2:1 stoichiometry (MnO :VCH) has been studied over a wide range of experimental conditions. It was found that the rate enhancement was associated with an increase in concentrations of alkali, reductant and temperature. A plausible mechanism involving an intermediate Mn(VII)-VCH complex (C) was expected and rate law is derived accordingly. Calculated activation parameters also supported the anticipated mechanism.

Kinetics between 5,10,15,20-tetrakis(N-methylpyridium-4-yl)porphyrin and Ni species were investigated in aqueous solution at 25 ±1 °C in = 0.10 M (NaNO). Speciation of Ni was done in = 0.10 M (NaNO) for knowing distribution of Ni species with solution pH. Experimental data were compared with speciation diagram constructed from the values of hydrolysis constants of Ni ion. Speciation data showed that hexaaquanickel(II) ions took place in hydrolysis reactions through formation of [Ni(OH)(OH)] species with solution pH. According to speciation of Ni and pH dependent rate constants, rate expression can be written as: d[Ni(TMPyP)]/dt = ( [Ni ] + [Ni(OH) ] + [Ni(OH) ] + [Ni(OH) ])[HTMPyP], where , , and were found to be = (0.62 ± 0.22) × 10; = (3.60 ± 0.40) × 10; = (2.09 ± 0.52) × 10, = (0.53 ± 0.04) × 10 Ms at 25 ±1 °C, respectively. Formation of hydrogen bonding between [Ni(HO)(OH)] and [HTMPyP] causes enhanced reactivity. Rate of formation of [Ni(II)TMPyP] complex was to be 3.99 × 10 Ms in = 0.10 M, NaNO (25 ± 1 °C). UV-Vis and fluorescence data suggested that [Ni(II)TMPyP] and [H(TMPyP)] interact with DNA outside binding with self-stacking and intercalation, respectively. .

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.

Many known and unknown factors play significant roles in the persistence of an infectious disease, but two that are often ignored in theoretical modelling are the distributions of (i) ( ) and (ii) ( ), in a population. While the former is determined by the immunity of an individual towards a disease, the latter depends on the exposure of a susceptible person to the infection. using a modified SAIR (Susceptible-Asymptomatic-Infected-Removed) model to include these two distributions. The resulting integro-differential equations are solved using Kinetic Monte Carlo Cellular Automata (KMC-CA) simulations. Ω infection occurs only if the value of Ω is greater than a Pandemic Infection Parameter (PIP), . Not only does this parameter provide a microscopic viewpoint of the reproduction number R advocated by the conventional SIR model, but it also takes into consideration the viral load experienced by a susceptible person. We find that the neglect of this coupling could compromise quantitative predictions and lead to incorrect estimates of the infections required to achieve the herd immunity threshold. The figure represents the network model for spread of infectious diseases considered in this work. It also shows the resultant multiwave infection graph by inclusion of inherent susceptibility and external infectivity distributions and migration of infected individuals.

Synthesis of several -oxides with tungsten exchanged hydroxyapatite (W/HAP) in the presence of 30% hydrogen peroxide (HO) as an oxidant is presented. A process with aqueous HO, a cheap and clean oxidant with an active catalyst is developed to reduce waste production and meet the requirements of green chemistry. Several tertiary amines have been efficiently oxidized to their corresponding -oxides with excellent yields. The as-synthesized catalyst (W/HAP) is characterized using BET, FTIR, SEM, ICP-OES and XRD. Effect of catalyst loading , temperature and oxidants were studied. A kinetic model has been developed to determine the reaction rate at different temperatures and activation energy for the model reaction.

Carbon nano onion (CNO) from dried grass has been synthesized by carbonization in the size range, 20 to 100 nm. This shows catalytic property to transform aerial oxygen under visible light to generate reactive oxygen species (ROS). A concept has been presented herein to show that this CNO even under room light generates hydrogen peroxide which inhibits WSN influenza virus (H1N1). The advantage of introducing CNO, synthesized from a cheap source to cater to the global need, is to sterilize infected hospitals indoor and outdoor, aircraft carriers, air conditioner vents due to its sustained conversion of air to ROS. Thus, CNO use could prevent frequent evacuation as used by conventional sanitisers to sterilize infected places from other RNA virus and hospital pathogens under COVID-19 pandemic. Carbon nano onion (CNO) under aerial oxygen on exposure with visible light generates ROS which is capable to rupture the lipid envelope of SARS-CoV-2 followed by disintegrating its RNA.

Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CL, PL, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail.

Nipah virus (NiV) and Hendra virus (HeV) are highly pathogenic paramyxovirus which belongs to Henipavirus family, causes severe respiratory disease, and may lead to fatal encephalitis infections in humans. NiV and HeV glycoproteins (G) bind to the highly conserved human ephrin-B2 and B3 (EFNB2 & EFNB3) cell surface proteins to mediate the viral entry. In this study, various molecular modelling approaches were employed to understand protein-protein interaction (PPI) of NiV and HeV glycoprotein (84% sequence similarity) with Human EFN (B2 and B3) to investigate the molecular mechanism of interaction at atomic level. Our computational study emphasized the PPI profile of both the viral glycoproteins with EFN (B2 and B3) in terms of non-bonded contacts, H-bonds, salt bridges, and identification of interface hotspot residues which play a critical role in the formation of complexes that mediate viral fusion and entry into the host cell. According to the reports, EFNB2 is considered to be more actively involved in the attachment with the NiV and HeV glycoprotein; interestingly the current computational study has displayed more conformational stability in HeV/NiV glycoprotein with EFNB2 complex with relatively high binding energy as compared to EFNB3. During the MD simulation, the number of H-bond formations was observed to be less in the case of EFNB3 complexes, which may be the possible reason for less conformational stability in the EFNB3 complexes. The current detailed interaction study on the PPI may put a path forward in designing peptide inhibitors to obstruct the interaction of viral glycoproteins with host proteins, thereby inhibiting viral entry.

Macrocyclic ligands (MacL-MacL) and Co(II) complexes were synthesized template condensation of o-phenylenediamine with various aromatic dicarboxylic acids. The elemental analysis, FT-IR, mass spectrometry, H NMR, C NMR, UV-vis, SEM analysis, powder X-ray diffraction, thermogravimetric (TG) analysis, electrochemical studies, and DFT analysis were used to characterize these synthesized ligands and their cobalt (II) complexes. TGA analysis to determine the stability and decomposition kinetic parameters. In element analysis, the percentage of different elements present and also the stoichiometry of compounds were confirmed. The proposed framework for tetraaza macrocyclic cobalt (II) complexes was supported by spectral analysis, which also revealed distorted octahedral geometry surrounding the central metal atom. The molecular structure of cobalt (II) complexes was also optimized theoretically, and their electronic or thermodynamic parameters were obtained from density functional theory (DFT). The synthesized ligands and their cobalt (II) complexes were tested against bacteria: Escherichia coli, Bacillus subtitles. were tested for antifungal properties. It was found that ligands and complexes show good antimicrobial results. Finally, using the Auto Dock VinaPyRx programme, molecular docking studies were used to evaluate the biological significance of the synthesized ligands to identify the probable and efficient binding mechanisms between the various ligands and the active site of the receptor protein.

Synthesis, characterization and theoretical studies of a novel coumarin-triazole-thiophene hybrid 4-(((4-ethyl-5-(thiophen-2-yl)-4-1,2,4-triazol-3-yl)thio)methyl)-6,7-dimethyl-2-chromen-2-one (), which was fabricated from 4-ethyl-5-(thiophen-2-yl)-4-1,2,4-triazole-3-thiol and 4-(chloromethyl)-6,7-dimethyl-2-chromen-2-one, are reported. The resulting compound was characterized by microanalysis, IR, H, and C APT NMR spectroscopy. The DFT calculations examined the structure and electronic properties of in gas phase. Its reactivity descriptors and molecular electrostatic potential revealed the reactivity and the reactive centers of . ADMET properties of were evaluated using the respective online tools. It was established that exhibit positive gastrointestinal absorption properties and negative human blood-brain barrier penetration. The Toxicity Model Report revealed that belongs to toxicity class 4. Molecular docking was additionally applied to study the interaction of with some SARS-CoV-2 proteins. It was established that the title compound is active against all the applied proteins with the most efficient interaction with Papain-like protease (PLpro). The interaction of with the applied proteins was also studied using molecular dynamics simulations.

More than 47,000 articles have been published in the area of Metal-Organic Framework since its seminal discovery in 1995, exemplifying the intense research carried out in this short span of time. Among other applications, gas adsorption and storage are perceived as central to the MOFs research, and more than 10,000 MOFs structures are reported to date to utilize them for various gas storage/separation applications. Molecular modeling, particularly based on density functional theory, played a key role in (i) understanding the nature of interactions between the gas and the MOFs geometry (ii) establishing various binding pockets and relative binding energies, and (iii) offering design clues to improve the gas uptake capacity of existing MOF architectures. In this review, we have looked at various MOFs that are studied thoroughly using DFT/periodic DFT (pDFT) methods for CO, H, O, and CH gases to provide a birds-eye-view on how various exchange-correlation functionals perform in estimating the binding energy for various gases and how factors such as nature of the (i) metal ion, (ii) linkers, (iii) ligand, (iv) spin state and (v) spin-couplings play a role in this process with selected examples. While there is still room for improvement, the rewards offered by the molecular modelling of MOFs were already substantial that we advocate experimental and theoretical studies to go hand-in-hand to undercut the trial-and-error approach that is often perceived in the selection of MOFs and gas partners in this area.