Computational prediction of toxicity of small organic molecules: state-of-the-art

2019 ◽  
Vol 4 (10) ◽  
Author(s):  
Janvhi Machhar ◽  
Ansh Mittal ◽  
Surendra Agrawal ◽  
Anil M. Pethe ◽  
Prashant S. Kharkar
Keyword(s):  
Organic Molecules ◽  
State Of The Art ◽  
Computational Prediction ◽  
Quality Data ◽  
End Points ◽  
Small Organic Molecules ◽  
New Chemical Entities ◽  
Other Substances ◽  
Modelling Techniques ◽  
Computational Resources

Abstract The field of computational prediction of various toxicity end-points has evolved over last two decades significantly. Availability of newer modelling techniques, powerful computational resources and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products and a lot of other substances. The field is still undergoing metamorphosis to take into account molecular complexities underlying toxicity end-points such as teratogenicity, mutagenicity, carcinogenicity, etc. Expansion of the applicability domain of these predictive models into areas other than life sciences, such as environmental and materials sciences have received a great deal of attention from all walks of life, fuelling further development and growth of the field. The present chapter discusses the state-of-the-art computational prediction of toxicity end-points of small organic molecules to balance the trade-off between the molecular complexity and the quality of such predictions, without compromising their immense utility in many fields.

scholarly journals Quantum Phases and Chern-Simons Geometrics for the generation of a ligand targeting COVID-19-SARS-COV-2 SPIKE D614G binding sites.

2021 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Drug Design ◽  
Structural Complexity ◽  
Rational Drug Design ◽  
Quality Data ◽  
Design Strategies ◽  
Rational Drug ◽  
New Chemical Entities ◽  
Other Substances ◽  
Computational Resources ◽  
Chern Simons

SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses infected ACE2-expressing cells pseudotyped with SG614 that is presently affecting a growing number of countries markedly more efficiently than those with SD614. The availability of newer powerful computational resources, molecular modeling techniques, and cheminformatics quality data have made it feasible to generate reliable algebraic calculations to design new chemical entities, merging chemicals, recoring natural products, and a lot of other substances fuelling further development and growth of this AI-quantum based drug design field to balance the trade-off between the structural complexity and the quality of such biophysics predictions that cannot be obtained by any other method. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule the RoccustyrnaTM small molecule, a multi-targeting druggable scaffold (1S,2R,3S)‐2‐({[(1S,2S,4S,5R)‐4‐ethenyl‐4‐sulfonylbicyclo[3.2.0]heptan‐2‐yl]oxy}amino)‐3‐[(2R,5R)‐5‐(2‐methyl‐6‐methylidene‐6,9‐dihydro‐3H‐purin‐9‐yl)‐3‐methylideneoxolan‐2‐yl]phosphirane‐1‐carbonitrile targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.

scholarly journals Chern-Simons Topology Geometrics for the generation of the RoccustyrnaTM molecule, a ligand targeting COVID-19-SARS-COV-2 SPIKE D614G binding sites

2020 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Decision Tree ◽  
Quality Data ◽  
Optimum Number ◽  
Active Chemical ◽  
New Chemical Entities ◽  
Other Substances ◽  
Computational Resources ◽  
Further Development ◽  
Chern Simons

Abstract SARS coronavirus 2 (SARS-CoV-2) encoding a SARS-COV-2 SPIKE D614G mutation in the viral spike (S) protein predominate over time in locales where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses pseudotyped with SG614 infected ACE2-expressing cells markedly more efficiently than those with SD614. The availability of newer modeling techniques, powerful computational resources, and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products, and a lot of other substances fuelling further development and growth of the field to balance the trade-off between the molecular complexity and the quality of such predictions that cannot be obtained by any other method. In this article, we effectively use a decision tree to obtain an optimum number of small chemical active chemical features from a collection of thousands of them utilizing a shallow neural network and jointly free energy cumulative feature ranking method with decision tree taking both network parameters and input toxicity benchmark features into account. In this paper, we strongly combine methods that are simple in machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels for the in-silico generation of the AI-Quantum designed molecule the RoccustyrnaTM small molecule, a multi-targeting druggable scaffold (1Z)-2‐{((2S,3S,5R)‐5‐ (2‐amino‐6‐oxo‐6,9‐dihydro‐1H‐purin‐9‐yl)‐3‐hydroxyoxolan‐2‐yl)methylidene}‐2‐cyano‐1‐({((2S,4R,5R)‐2‐methyl‐2‐(methylamino)‐1,6‐diazabicyclo(3.2.0)heptan‐4‐yl)oxy}imino)‐1lambda5,2lambda5‐azaphosphiridin‐1‐ylium.targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.

scholarly journals AI-Quantum Phases on Chern-Simons Topological Geometrics for the Generation of A Ligand Targeting COVID-19-SARS-COV-2 SPIKE D614G Binding Sites.

2021 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Drug Design ◽  
Structural Complexity ◽  
Rational Drug Design ◽  
Quality Data ◽  
Design Strategies ◽  
Rational Drug ◽  
New Chemical Entities ◽  
Other Substances ◽  
Computational Resources ◽  
Chern Simons

Abstract SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses infected ACE2-expressing cells pseudotyped with SG614 that is presently affecting a growing number of countries markedly more efficiently than those with SD614. The availability of newer powerful computational resources, molecular modeling techniques, and cheminformatics quality data have made it feasible to generate reliable algebraic calculations to design new chemical entities, merging chemicals, fragmentizing natural products, and a lot of other substances fuelling further development and growth of this AI-quantum based drug design field to balance the trade-off between the structural complexity and the quality of such biophysics predictions that cannot be obtained by any other method. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule the RoccustyrnaTM small molecule, a multi-targeting druggable scaffold (1S,2R,3S)‐2‐({[(1S,2S,4S,5R)‐4‐ethenyl‐4‐sulfonylbicyclo[3.2.0] heptan‐2‐yl]oxy}amino)‐3‐[(2R,5R)‐5‐(2‐methyl‐6‐methylidene‐6,9‐dihydro‐3H‐purin‐9‐yl)‐3‐methylideneoxolan‐2‐yl]phosphirane‐1‐carbonitrile targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.

scholarly journals Topology GeometRics for the generation of the RoccustyrnaTM molecule, a ligand targeted COVID-19-D614G sites.

2020 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Decision Tree ◽  
Quality Data ◽  
Optimum Number ◽  
Active Chemical ◽  
New Chemical Entities ◽  
Other Substances ◽  
Computational Resources ◽  
Further Development ◽  
Small Chemical

Abstract SARS coronavirus 2 (SARS-CoV-2) encoding a D614G mutation in the viral spike (S) protein predominate over time in locales where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses pseudotyped with SG614 infected ACE2-expressing cells markedly more efficiently than those with SD614. The availability of newer modeling techniques, powerful computational resources, and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products, and a lot of other substances fuelling further development and growth of the field to balance the trade-off between the molecular complexity and the quality of such predictions that cannot be obtained by any other method. In this article, we effectively use a decision tree to obtain an optimum number of small chemical active chemical features from a collection of thousands of them utilizing a shallow neural network and jointly free energy cumulative feature ranking method with decision tree taking both network parameters and input toxicity benchmark features into account. In this paper, we strongly combine the toxic models and ADMET methods that are simple in machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels for the in-silico generation of the RoccustyrnaTM small molecule, a less toxic nano-ligand targeted the COVID-19-D614G mutation using Topology Euclidean Geometric and Artificial Intelligence-Driven Predictive Neural Networks. To demonstrate this, we also develop a Gravitational Topological (UFs) based Quantum-Parallel Particle Swarm Inspired framework using only 2D chemical features that are less compute- intensive.

scholarly journals Ferroelectric columnar assemblies from the bowl-to-bowl inversion of aromatic cores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shunsuke Furukawa ◽  
Jianyun Wu ◽  
Masaya Koyama ◽  
Keisuke Hayashi ◽  
Norihisa Hoshino ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Organic Molecule ◽  
Remanent Polarization ◽  
Organic Molecules ◽  
Materials Science ◽  
State Of The Art ◽  
Condensed Matter ◽  
Small Organic Molecules ◽  
Organic Ferroelectrics ◽  
Potential Use

AbstractOrganic ferroelectrics, in which the constituent molecules retain remanent polarization, represent an important topic in condensed-matter science, and their attractive properties, which include lightness, flexibility, and non-toxicity, are of potential use in state-of-the-art ferroelectric devices. However, the mechanisms for the generation of ferroelectricity in such organic compounds remain limited to a few representative concepts, which has hitherto severely hampered progress in this area. Here, we demonstrate that a bowl-to-bowl inversion of a relatively small organic molecule with a bowl-shaped π-aromatic core generates ferroelectric dipole relaxation. The present results thus reveal an unprecedented concept to produce ferroelectricity in small organic molecules, which can be expected to strongly impact materials science.

CO2-based hydrogen storage: CO2 hydrogenation to formic acid, formaldehyde and methanol

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Thomas Schaub
Keyword(s):  
Hydrogen Storage ◽  
Formic Acid ◽  
Mobile Applications ◽  
Organic Molecules ◽  
State Of The Art ◽  
Co2 Hydrogenation ◽  
The State ◽  
Reverse Reaction ◽  
Small Organic Molecules ◽  
Hydrogenation Of Co

AbstractThe storage of hydrogen via hydrogenation of CO2to small organic molecules can be attractive for mobile applications. In this article, the state of the art regarding hydrogen storage in Methanol, Formic Acid as well as Formaldehyde and derivates based on CO2hydrogenation is summarized. The reverse reaction, the release of hydrogen from these molecules is also crucial and described in the articles together with possible concepts for the use of hydrogen storage by CO2hydrogenation.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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