scholarly journals Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elisabeth J. Schiessler ◽  
Tim Würger ◽  
Sviatlana V. Lamaka ◽  
Robert H. Meißner ◽  
Christian J. Cyron ◽  
...  
Keyword(s):  
Corrosion Inhibition ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Automatic Identification ◽  
Organic Additives ◽  
Statistical Tool ◽  
Degradation Behaviour ◽  
Corrosion Inhibition Efficiency ◽  
Selection Of

AbstractThe degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

scholarly journals A Chemographic Audit of Anti-Coronavirus Structure-Activity Information from Public Databases (ChEMBL)

2020 ◽  
Author(s):  
Dragos Horvath ◽  
Alexey Orlov ◽  
Dmitry Osolodkin ◽  
Gilles Marcou ◽  
Aydar A. Ishmukhametov Ishmukhametov ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Chemical Space ◽  
Viral Proteins ◽  
Virus Species ◽  
Generative Topographic Mapping ◽  
Structure Activity ◽  
Definition Of ◽  
Pathogenic Agents ◽  
Activity Information ◽  
Selection Of

<p>Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. </p> The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti-CoV” map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800M organic compounds.

scholarly journals A Chemographic Audit of Anti-Coronavirus Structure-Activity Information from Public Databases (ChEMBL)

2020 ◽  
Author(s):  
Dragos Horvath ◽  
Alexey Orlov ◽  
Dmitry Osolodkin ◽  
Gilles Marcou ◽  
Aydar A. Ishmukhametov Ishmukhametov ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Chemical Space ◽  
Viral Proteins ◽  
Virus Species ◽  
Generative Topographic Mapping ◽  
Structure Activity ◽  
Definition Of ◽  
Pathogenic Agents ◽  
Activity Information ◽  
Selection Of

<p>Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. </p> The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti-CoV” map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800M organic compounds.

Adsorption and corrosion inhibition characteristics of two medicinal molecules

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Hydrochloric Acid ◽  
Corrosion Rate ◽  
Mild Steel ◽  
Corrosion Inhibition ◽  
Half Life ◽  
Thermodynamic Data ◽  
Inhibition Efficiency ◽  
Gravimetric Method ◽  
Corrosive Environment ◽  
Corrosion Inhibition Efficiency

The corrosion inhibition characteristics of two medicinal molecules phenylalanine and rutin on mild steel in 1.0M Hydrochloric acid were evaluated using gravimetric method. Corrosion inhibition efficiency of 83.78 and 90.40 % was obtained respectively after seven days. However, phenylalanine showed weak accumulative higher corrosion inhibition efficiency. The presence of both molecules in the corrosive environment reduced the corrosion rate constant and increased the material half-life. Thermodynamic data calculated suggests a spontaneous adsorption of the molecules on the mild steel’s surface.

scholarly journals An Overview of Molecular Dynamic Simulation for Corrosion Inhibition of Ferrous Metals

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Nur Izzah Nabilah Haris ◽  
Shafreeza Sobri ◽  
Yus Aniza Yusof ◽  
Nur Kartinee Kassim
Keyword(s):  
Molecular Dynamic ◽  
Corrosion Inhibition ◽  
High Performance ◽  
Md Simulation ◽  
Ferrous Metal ◽  
Working Mechanism ◽  
Inhibitor Molecule ◽  
As Adsorption ◽  
Inhibition Studies ◽  
Selection Of

Molecular dynamics (MD) simulation is a powerful tool to study the molecular level working mechanism of corrosion inhibitors in mitigating corrosion. In the past decades, MD simulation has emerged as an instrument to investigate the interactions at the interface between the inhibitor molecule and the metal surface. Combined with experimental measurement, theoretical examination from MD simulation delivers useful information on the adsorption ability and orientation of the molecule on the surface. It relates the microscopic characteristics to the macroscopic properties which enables researchers to develop high performance inhibitors. Although there has been vast growth in the number of studies that use molecular dynamic evaluation, there is still lack of comprehensive review specifically for corrosion inhibition of organic inhibitors on ferrous metal in acidic solution. Much uncertainty still exists on the approaches and steps in performing MD simulation for corrosion system. This paper reviews the basic principle of MD simulation along with methods, selection of parameters, expected result such as adsorption energy, binding energy and inhibitor orientation, and recent publications in corrosion inhibition studies.

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

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