scholarly journals Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System

2019 ◽  
Author(s):  
Yasuharu Okamoto
Keyword(s):  
In Silico ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Database Search ◽  
Montreal Protocol ◽  
Chemical Calculation ◽  
In Silico Screening ◽  
Low Global Warming Potential ◽  
Non Destructive

<p>To comply with the Kigali amendment to the Montreal Protocol in 2016, it becomes an urgent matter to develop new refrigerants with low global warming potential with simultaneously meeting conventional requirements of cooling performance, safety, and non-destructive to the ozone layer. Because each requirement links to different chemical property, proper control of various chemical properties is necessary to achieve the requirements. However, it seems to be extremely difficult to satisfy all the requirements simultaneously due to the tradeoffs among the properties. For this reason, we need to correctly recognize how these chemical properties behave when the composition of molecule is changed. Here we have done in silico screening that combines quantum chemical calculation, machine learning, and database search, where 10,163 molecules were exhaustively investigated within the properly imposed constraints, then we have found several candidates for new refrigerants. It should be noted that the synthesis of refrigerants is more difficult than that of ordinary organic molecules because glassware cannot be used for the synthesis of fluorine-containing molecules that most refrigerants apply. This makes in silico screening a more useful approach in the design of refrigerants. </p>

scholarly journals Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System

2019 ◽  
Author(s):  
Yasuharu Okamoto
Keyword(s):  
In Silico ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Database Search ◽  
Montreal Protocol ◽  
Chemical Calculation ◽  
In Silico Screening ◽  
Low Global Warming Potential ◽  
Non Destructive

<p>To comply with the Kigali amendment to the Montreal Protocol in 2016, it becomes an urgent matter to develop new refrigerants with low global warming potential with simultaneously meeting conventional requirements of cooling performance, safety, and non-destructive to the ozone layer. Because each requirement links to different chemical property, proper control of various chemical properties is necessary to achieve the requirements. However, it seems to be extremely difficult to satisfy all the requirements simultaneously due to the tradeoffs among the properties. For this reason, we need to correctly recognize how these chemical properties behave when the composition of molecule is changed. Here we have done in silico screening that combines quantum chemical calculation, machine learning, and database search, where 10,163 molecules were exhaustively investigated within the properly imposed constraints, then we have found several candidates for new refrigerants. It should be noted that the synthesis of refrigerants is more difficult than that of ordinary organic molecules because glassware cannot be used for the synthesis of fluorine-containing molecules that most refrigerants apply. This makes in silico screening a more useful approach in the design of refrigerants. </p>

scholarly journals Quantum Chemistry Meets Machine Learning

2019 ◽  
Vol 73 (12) ◽  
pp. 983-989 ◽  
Author(s):  
Alberto Fabrizio ◽  
Benjamin Meyer ◽  
Raimon Fabregat ◽  
Clemence Corminboeuf
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Quantum Chemical ◽  
Large Scale ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Learning Approaches ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Large Scale Screening

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

scholarly journals Synthesis and in silico screening of a library of β-carboline-containing compounds

2012 ◽  
Vol 8 ◽  
pp. 1048-1058 ◽  
Author(s):  
Kay M Brummond ◽  
John R Goodell ◽  
Matthew G LaPorte ◽  
Lirong Wang ◽  
Xiang-Qun Xie
Keyword(s):  
In Silico ◽  
Therapeutic Potential ◽  
Chemical Properties ◽  
Mitogen Activated Protein Kinase ◽  
In Silico Screening ◽  
Mitogen Activated Protein ◽  
Histone Deacetylase 4 ◽  
Synthetic Intermediates ◽  
Oxide Synthase ◽  
Endothelial Nitric Oxide

The synthesis of a library of tetrahydro-β-carboline-containing compounds in milligram quantities is described. Among the unique heterocyclic frameworks are twelve tetrahydroindolizinoindoles, six tetrahydrocyclobutanindoloquinolizinones and three tetrahydrocyclopentenoneindolizinoindolones. These compounds were selected from a virtual combinatorial library of 11,478 compounds. Physical chemical properties were calculated and most of them are in accordance with Lipinski’s rules. Virtual docking and ligand-based target evaluations were performed for the β-carboline library compounds and selected synthetic intermediates to assess the therapeutic potential of these small organic molecules. These compounds have been deposited into the NIH Molecular Repository (MLSMR) and may target proteins such as histone deacetylase 4, endothelial nitric oxide synthase, 5-hydroxytryptamine receptor 6 and mitogen-activated protein kinase 1. These in silico screening results aim to add value to the β-carboline library of compounds for those interested in probes of these targets.

scholarly journals Physico-Chemical Properties and Quantum Chemical Calculation of 2-methoxy-4-(prop-2-en-1-yl) phenol (EUGENOL)

2021 ◽  
pp. 01-22
Author(s):  
Dr. Raksha Gupta
Keyword(s):  
Quantum Chemical ◽  
Population Analysis ◽  
Chemical Properties ◽  
Geometric Optimization ◽  
Oscillator Strengths ◽  
Basis Set ◽  
Chemical Calculation ◽  
Excitation Energies ◽  
Mulliken Population ◽  
Physico Chemical

Physico-chemical properties plays an important role in determining toxicity of a material hence were calculated using acdlab/chemsketch and the data predicted is generated using ACD/Labs Percepta Platform - PhysChem Module. Gaussian 09, RevisionA.01, software package was used for the theoretical quantum chemical calculations of 2-methoxy-4-(prop-2-en-1-yl) phenol commonly called Eugenol. DFT/B3LYP/6-311G (d, p) basis was used to perform geometric optimization and vibrational frequency determination of the molecule. The statistical thermochemical calculations of the molecule were done at DFT/B3LYP/6-311G (d, p) basis set to calculate the standard thermodynamic functions: heat capacity (CV), entropy (S) and Enthalpy (E). DFT/B3LYP/6-311G (d, p) basis set was used to calculate the various NLO properties like dipole moment (µ), mean linear polarizability (α), anisotropic polarizability (Δα), first order hyperpolarizability (β), second order hyperpolarizability (γ) in terms of x, y, z components for Eugenol (2-methoxy-4-(prop-2-en-1-yl) phenol. Same basis set was used to carry out Mulliken population analysis. UV-Visible absorption spectra, ECD spectra, electronic transitions, vertical excitation energies and oscillator strengths of Eugenol (2-methoxy-4-(prop-2-en-1-yl) phenol) were computed by Time Dependent DFT (TD-DFT) method using the same basis set. FMO analysis, Molecular electrostatic potential study was also done using the same basis set.

Examination on Permittivity Estimation of Hydrocarbon-based Thermosetting Resin based on Quantum Chemical Calculation

2017 ◽  
Vol 137 (11) ◽  
pp. 626-631 ◽  
Author(s):  
Yuki Fuchi ◽  
Ryota Nakasako ◽  
Masahiro Kozako ◽  
Masayuki Hikita ◽  
Nobuhito Kamei
Keyword(s):  
Quantum Chemical Calculation ◽  
Quantum Chemical ◽  
Chemical Calculation ◽  
Thermosetting Resin

Gold-Aptamer-Nanoconstructs Engineered to Detect Conserved Enteroviral Nucleic Acid Sequences

2019 ◽  
Author(s):  
Veeren Chauhan ◽  
Mohamed M Elsutohy ◽  
C Patrick McClure ◽  
Will Irving ◽  
Neil Roddis ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
In Silico ◽  
Point Of Care ◽  
Lateral Flow ◽  
Nucleic Acid Sequence ◽  
In Silico Screening ◽  
Lateral Flow Assays ◽  
Life Threatening ◽  
Software And Hardware ◽  
Nucleic Acid Sequences

<p>Enteroviruses are a ubiquitous mammalian pathogen that can produce mild to life-threatening disease. Bearing this in mind, we have developed a rapid, accurate and economical point-of-care biosensor that can detect a nucleic acid sequences conserved amongst 96% of all known enteroviruses. The biosensor harnesses the physicochemical properties of gold nanoparticles and aptamers to provide colourimetric, spectroscopic and lateral flow-based identification of an exclusive enteroviral RNA sequence (23 bases), which was identified through in silico screening. Aptamers were designed to demonstrate specific complementarity towards the target enteroviral RNA to produce aggregated gold-aptamer nanoconstructs. Conserved target enteroviral nucleic acid sequence (≥ 1x10<sup>-7</sup> M, ≥1.4×10<sup>-14</sup> g/mL), initiates gold-aptamer-nanoconstructs disaggregation and a signal transduction mechanism, producing a colourimetric and spectroscopic blueshift (544 nm (purple) > 524 nm (red)). Furthermore, lateral-flow-assays that utilise gold-aptamer-nanoconstructs were unaffected by contaminating human genomic DNA, demonstrated rapid detection of conserved target enteroviral nucleic acid sequence (< 60 s) and could be interpreted with a bespoke software and hardware electronic interface. We anticipate our methodology will translate in-silico screening of nucleic acid databases to a tangible enteroviral desktop detector, which could be readily translated to related organisms. This will pave-the-way forward in the clinical evaluation of disease and complement existing strategies at overcoming antimicrobial resistance.</p>

Design and in-silico screening of Peptide Nucleic Acid (PNA) inspired novel pronucleotide scaffolds targeting COVID-19

2020 ◽  
Vol 16 ◽  
Author(s):  
Bichismita Sahu ◽  
Santosh Kumar Behera ◽  
Rudradip Das ◽  
Tanay Dalvi ◽  
Arnab Chowdhury ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
In Silico ◽  
Peptide Nucleic Acid ◽  
Large Set ◽  
Nonstructural Proteins ◽  
In Silico Screening ◽  
Replication Process ◽  
Enveloped Virus ◽  
Treatment Regimens ◽  
Novel Coronavirus

Introduction: The outburst of the novel coronavirus COVID-19, at the end of December 2019 has turned itself into a pandemic taking a heavy toll on human lives. The causal agent being SARS-CoV-2, a member of the long-known Coronaviridae family, is a positive sense single-stranded enveloped virus and quite closely related to SARS-CoV. It has become the need of the hour to understand the pathophysiology of this disease, so that drugs, vaccines, treatment regimens and plausible therapeutic agents can be produced. Methods: In this regard, recent studies uncovered the fact that the viral genome of SARS-CoV-2 encodes nonstructural proteins like RNA dependent RNA polymerase (RdRp) which is an important tool for its transcription and replication process. A large number of nucleic acid based anti-viral drugs are being repurposed for treating COVID-19 targeting RdRp. Few of them are in the advanced stage of clinical trials including Remdesivir. While performing close investigation of the large set of nucleic acid based drugs, we were surprised to find that the synthetic nucleic acid backbone is explored very little or rare. Results: We have designed scaffolds derived from peptide nucleic acid (PNA) and subjected them for in-silico screening systematically. These designed molecules have demonstrated excellent binding towards RdRp. Compound 12 was found to possess similar binding affinity as Remdesivir with comparable pharmacokinetics. However, the in-silico toxicity prediction indicates compound 12 may be a superior molecule which can be explored further due to its excellent safety-profile with LD50 (12,000mg/kg) as opposed to Remdesivir (LD50 =1000mg/kg). Conclusion: Compound 12 falls in the safe category of class 6. Synthetic feasibility, equipotent binding and very low toxicity of this peptide nucleic acid derived compounds can serve as a leading scaffold to design, synthesize and evaluate many of similar compounds for the treatment of COVID-19.

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