Molecular features of sulfonic acids used for synthesis of overbased detergent additives

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

10.26434/chemrxiv.9913865.v2 ◽

2019 ◽

Author(s):

Qi Yuan ◽

Alejandro Santana-Bonilla ◽

Martijn Zwijnenburg ◽

Kim Jelfs

Keyword(s):

Neural Network ◽

Electronic Properties ◽

Transfer Learning ◽

Recurrent Neural Network ◽

Chemical Space ◽

Generative Models ◽

Molecular Features ◽

Donor Acceptor ◽

Homo Lumo ◽

Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Physiological and molecular features of allelopathic and non-allelopathic rice in response to weed competition

Allelopathy Journal ◽

10.26651/allelo.j/2019-47-2-1229 ◽

2019 ◽

Vol 47 (2) ◽

pp. 167-180

Author(s):

H B. Wang ◽

Q. Zhang ◽

Z H. Lin ◽

J. Y. Li ◽

S X. Lin ◽

...

Keyword(s):

Weed Competition ◽

Molecular Features

Cellular and molecular features of endometrial hyperplasia under the influence of industrial toxic factors

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-690-691 ◽

2020 ◽

pp. 690-690

Author(s):

I. O. Marinkin ◽

E. S. Lisova ◽

V. V. Evchenko

Keyword(s):

Oxidative Stress ◽

Growth Factor ◽

Endometrial Hyperplasia ◽

Transforming Growth Factor ◽

Length Of Service ◽

Ki67 Expression ◽

Molecular Features ◽

Reproductive Toxicants ◽

Cd34 Expression ◽

And Oxidative Stress

The features of biomechanisms of endometrial hyperplasia in subjects exposed to reproductive toxicants were inflammation and oxidative stress. An association of Ki67 expression with 8-hydroxydeoxyguanosine, length of service, CD34 expression with 8-isoprostane and both Ki67 and CD34 expression with transforming growth factor B1 and lead exposure established.

N-Sulfonic Acids: New, Efficient and Reusable Catalysts for the Acceleration of Organic Reactions

Current Organic Chemistry ◽

10.2174/1385272819666150629174946 ◽

2015 ◽

Vol 19 (20) ◽

pp. 2011-2039 ◽

Cited By ~ 6

Author(s):

Masoumeh Abedini ◽

Farhad Shirini ◽

Azadeh Shahriari

Keyword(s):

Organic Reactions ◽

Sulfonic Acids

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200902141907 ◽

2020 ◽

Vol 23 ◽

Author(s):

Apilak Worachartcheewan ◽

Alla P. Toropova ◽

Andrey A. Toropov ◽

Reny Pratiwi ◽

Virapong Prachayasittikul ◽

...

Keyword(s):

Histone Deacetylases ◽

Rational Design ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Sirtuin 1 ◽

Data Set ◽

Functional Roles ◽

Molecular Features ◽

Oecd Principles ◽

Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Quantitative Analysis of Essential Molecular Features of Coumarin Derivatives with Antioxidant Activity Using Chemometric Tools

Current Computer - Aided Drug Design ◽

10.2174/1573409912666160721153935 ◽

2016 ◽

Vol 12 (3) ◽

pp. 241-250 ◽

Cited By ~ 1

Author(s):

Biplab De ◽

Indrani Adhikari ◽

Ashis Nandy ◽

Achintya Saha ◽

Binoy Goswami

Keyword(s):

Antioxidant Activity ◽

Quantitative Analysis ◽

Coumarin Derivatives ◽

Molecular Features ◽

Chemometric Tools

Clinicopathologic and Molecular Features of a Series of 41 Biphenotypic Sinonasal Sarcomas Expanding Their Molecular Spectrum

The American Journal of Surgical Pathology ◽

10.1097/pas.0000000000001238 ◽

2019 ◽

Vol 43 (6) ◽

pp. 747-754 ◽

Cited By ~ 15

Author(s):

François Le Loarer ◽

Sophie Laffont ◽

Tom Lesluyes ◽

Franck Tirode ◽

Cristina Antonescu ◽

...

Keyword(s):

Molecular Spectrum ◽

Molecular Features