The Connectivity Stack, a New Format for Representation of Organic Chemical Structures

1974 ◽  
Vol 14 (4) ◽  
pp. 200-202 ◽  
Author(s):  
Yoshihiro Kudo ◽  
Shin-ichi Sasaki
Keyword(s):  
Organic Chemical ◽  
Chemical Structures

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

Brief Organic Review

2016 ◽  
Author(s):  
Gary W. Morrow
Keyword(s):  
Functional Groups ◽  
Lewis Acids ◽  
Three Dimensional ◽  
Polarized Light ◽  
Mirror Image ◽  
Bond Line ◽  
Organic Chemical ◽  
Chemical Structures ◽  
Or Groups ◽  
Standard Formalism

In addition to simple hydrocarbon structures (alkanes, alkenes, alkynes, and aromatic systems) and alkyl groups (methyl, ethyl, propyl, isopropyl, etc.), this text assumes a familiarity with the most common functional groups associated with organic chemical structures and their basic reactivity patterns. Table 1.1 summarizes the names and structures of some of the more important functional groups we will be dealing with throughout the remainder of the book. It is important to remember that functional groups containing O or N with nonbonding electrons have an affinity for both protic and Lewis acids and are important participators in H-bonding. Groups containing a carbonyl (C=O) function are especially important, as these bonds are strongly polarized (δ+C=Oδ–), the C atom being electron deficient and the O atom electron excessive; this strong polarization is mainly responsible for the familiar reactivity patterns associated with carbonyl compounds. Figure 1.1 depicts the standard classification of isomers in organic chemical structures. Recall that constitutional isomers are compounds with the same molecular formula but different atom connectivity, such as 1-butanol versus 2-butanol. Stereoisomers, on the other hand, are compounds with the same formula and the same atom connectivity, differing from one another only in the three-dimensional orientation of their atoms in space. These are divided into two groups: enantiomers and diastereomers. Enantiomers are nonsuperimposable mirror image molecules whose asymmetry is usually the result of a tetrahedral carbon atom with four different atoms or groups attached to it, as in the 2-butanol enantiomers. Such chiral molecules rotate the plane of polarized light either (+) or (−) and so are said to be optically active. Achiral molecules, such as 1-butanol, do not rotate the plane of polarized light and so are optically inactive. A standard formalism for representation of a chiral center is to use bond line drawings with two of the four atoms or groups lying in the plane of the paper, a third projecting outward (wedge bond), and the fourth projecting inward (dashed bond).

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

The Impact of Adding Nitrogen Substitutes to Conventional Automotive Fuels

1999 ◽  
Vol 121 (3) ◽  
pp. 225-230
Author(s):  
S. Gouli ◽  
A. Serdari ◽  
S. Stournas ◽  
E. Lois
Keyword(s):  
Public Awareness ◽  
Operating Conditions ◽  
Pollutant Emissions ◽  
Organic Chemical ◽  
Transportation Fuels ◽  
Oxygenated Compounds ◽  
Chemical Structures ◽  
Ignition Quality ◽  
Oil Crises ◽  
The Impact

The adoption of oxygenates in gasoline was originally spurred by the oil crises of the 1970s. In more recent years, public awareness of the environmental issues constituted the main reason for the spreading of oxygenated compounds in the transportation fuels sector. This paper describes the effects of novel nitrogen compounds in gasoline and diesel fuel on ignition quality and on pollutant emissions. Our intention is to investigate the antiknock quality, as gaged by octane and cetane determinations, of organic chemical structures mostly derivable from biomass, in combination with their effectiveness in reducing exhaust emissions under various operating conditions.

ChemInform Abstract: XyM Notation for Electronic Communication of Organic Chemical Structures.

ChemInform ◽  
2010 ◽  
Vol 31 (5) ◽  
pp. no-no
Author(s):  
Shinsaku Fujita ◽  
Nobuya Tanaka
Keyword(s):  
Electronic Communication ◽  
Organic Chemical ◽  
Chemical Structures

scholarly journals Data-Driven UPLC-Orbitrap MS Analysis in Astrochemistry

Life ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 35 ◽  
Author(s):  
Alexander Ruf ◽  
Pauline Poinot ◽  
Claude Geffroy ◽  
Louis Le Sergeant d’Hendecourt ◽  
Gregoire Danger
Keyword(s):  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Data Driven ◽  
Organic Chemical ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Chemical Structures ◽  
High Resolution Mass ◽  
Orbitrap Ms ◽  
Resolution Mass

Meteorites have been found to be rich and highly diverse in organic compounds. Next to previous direct infusion high resolution mass spectrometry experiments (DI-HR-MS), we present here data-driven strategies to evaluate UPLC-Orbitrap MS analyses. This allows a comprehensive mining of structural isomers extending the level of information on the molecular diversity in astrochemical materials. As a proof-of-concept study, Murchison and Allende meteorites were analyzed. Both, global organic fingerprint and specific isomer analyses are discussed. Up to 31 different isomers per molecular composition are present in Murchison suggesting the presence of ≈440,000 different compounds detected therein. By means of this time-resolving high resolution mass spectrometric method, we go one step further toward the characterization of chemical structures within complex extraterrestrial mixtures, enabling a better understanding of organic chemical evolution, from interstellar ices toward small bodies in the Solar System.

XyM Notation for Electronic Communication of Organic Chemical Structures

1999 ◽  
Vol 39 (6) ◽  
pp. 903-914 ◽  
Author(s):  
Shinsaku Fujita ◽  
Nobuya Tanaka
Keyword(s):  
Electronic Communication ◽  
Organic Chemical ◽  
Chemical Structures

scholarly journals NMR. Issues in configuration determination

2016 ◽  
Author(s):  
Olga Staszewska-Krajewska
Keyword(s):  
Carbon Atom ◽  
Dipolar Coupling ◽  
Nuclear Overhauser Effect ◽  
Chemical Shifts ◽  
Coupling Constants ◽  
New Drugs ◽  
Organic Chemical ◽  
Simple Relationship ◽  
Relative Configuration ◽  
Chemical Structures

It is well known that the biological properties of compounds are directly related to the configuration of their stereogenic centers. Therefore, the determination of the compound spatial structure, including its configuration, is crucial for better understanding of the mode of action of bioactive compounds and the design of new drugs. The nuclear Overhauser effect has been shown to be highly useful in NMR spectroscopy for characterizing and refining organic chemical structures. However, the results are not always conclusive. During the presentation the most common problems associated with the acquisition and interpretation of NOE data will be discussed. Other alternative measurement parameters (chemical shifts and coupling constants 3JH-H), which are usually neglected when assigning configuration, will be presented. Also RDS (residuel dipolar coupling) measurements, that are an alternative to NOE measurements, will be briefly explained. The second part of the presentation will be devoted to determining the relative configuration of β-lactams. Recently we have found that the assignment of relative configuration at the bridgehead carbon atom of bicyclic carbapenams can be easily achieved by analyzing chemical shifts of the geminal protons of the methylene group at the C-3 carbon atom. This observation can be attributed to the influence of the anisotropy of the neighboring carbonyl group and can be easily correlated with the configuration at the bridgehead carbon atom. The study showed that the proposed simple relationship can also be used for other azabicyclic compounds.

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