scholarly journals A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

2021 ◽  
Author(s):  
Tobias Gensch ◽  
Gabriel dos Passos Gomes ◽  
Pascal Friederich ◽  
Ellyn Peters ◽  
Theophile Gaudin ◽  
...  
Keyword(s):  
Structure Property ◽  
Vast Number ◽  
Structure Property Relationships ◽  
Reaction Optimization ◽  
Physicochemical Descriptors ◽  
Mechanical Methods ◽  
Quantitative Structure Property Relationships ◽  
Property Space ◽  
Novel Catalyst ◽  
Molecular Catalysts

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

scholarly journals A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

2021 ◽  
Author(s):  
Tobias Gensch ◽  
Gabriel dos Passos Gomes ◽  
Pascal Friederich ◽  
Ellyn Peters ◽  
Theophile Gaudin ◽  
...  
Keyword(s):  
Structure Property ◽  
Vast Number ◽  
Structure Property Relationships ◽  
Reaction Optimization ◽  
Physicochemical Descriptors ◽  
Mechanical Methods ◽  
Quantitative Structure Property Relationships ◽  
Property Space ◽  
Novel Catalyst ◽  
Molecular Catalysts

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

scholarly journals Prediction of Parallel Artificial Membrane Permeability Assay of Some Drugs from their Theoretically Calculated Molecular Descriptors

2011 ◽  
Vol 8 (3) ◽  
pp. 1074-1085
Author(s):  
E. Konoz ◽  
Amir H. M. Sarrafi ◽  
S. Ardalani
Keyword(s):  
Neural Network ◽  
Membrane Permeability ◽  
Selection Procedure ◽  
Structure Property ◽  
Artificial Membrane ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Artificial Neural ◽  
Variable Subset Selection ◽  
Mlr Model

Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural network model in the prediction of membrane permeability of drugs.

Characteristic study of irregularity measures of some nanotubes

2019 ◽  
Vol 97 (10) ◽  
pp. 1125-1132 ◽  
Author(s):  
Zahid Iqbal ◽  
Adnan Aslam ◽  
Muhammad Ishaq ◽  
Muhammad Aamir
Keyword(s):  
Quantitative Structure ◽  
Structure Property ◽  
Know How ◽  
Molecular Graphs ◽  
Irregularity Index ◽  
Structure Property Relationships ◽  
Boiling Points ◽  
Quantitative Structure Property Relationships ◽  
Quantitative Structure Activity Relationships ◽  
Vertex Degrees

In many applications and problems in material engineering and chemistry, it is valuable to know how irregular a given molecular structure is. Furthermore, measures of the irregularity of underlying molecular graphs could be helpful for quantitative structure property relationships and quantitative structure-activity relationships studies, and for determining and expressing chemical and physical properties, such as toxicity, resistance, and melting and boiling points. Here we explore the following three irregularity measures: the irregularity index by Albertson, the total irregularity, and the variance of vertex degrees. Using graph structural analysis and derivation, we compute the above-mentioned irregularity measures of several molecular graphs of nanotubes.

Aqueous-phase photooxygenation of enes, amines, sulfides and polycyclic aromatics by singlet (a1Δg) oxygen: prediction of rate constants using orbital energies, substituent factors and quantitative structure–property relationships

2017 ◽  
Vol 14 (7) ◽  
pp. 442 ◽  
Author(s):  
Tom M. Nolte ◽  
Willie J. G. M. Peijnenburg
Keyword(s):  
Singlet Oxygen ◽  
Molecular Orbital ◽  
Organic Solvents ◽  
Aqueous Phase ◽  
Rate Constant ◽  
Rate Constants ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships

Environmental contextTo aid the transition to sustainable chemistry there is a need to improve the degradability of chemicals and limit the use of organic solvents. Singlet oxygen, 1O2, is involved in organic synthesis and photochemical degradation; however, information on its aqueous-phase reactivity is limited. We developed cheminformatics models for photooxidation rate constants that will enable accurate assessment of aquatic photochemistry without experimentation. AbstractTo aid the transition to sustainable and green chemistry there is a general need to improve the degradability of chemicals and limit the use of organic solvents. In this study we developed quantitative structure–property relationships (QSPRs) for aqueous-phase photochemical reactions by singlet (a1Δg) oxygen. The bimolecular singlet oxygen reaction rate constant can be reliably estimated (R2 = 0.73 for naphtalenes and anthracenes, R2 = 0.86 for enes and R2 = 0.88 for aromatic amines) using the energy of the highest occupied molecular orbital (EHOMO). Additional molecular descriptors were used to characterise electronic and steric factors influencing the rate constant for aromatic enes (R2 = 0.74), sulfides and thiols (R2 = 0.72) and aliphatic amines. Mechanistic principles (frontier molecular orbital, perturbation and transition state theories) were applied to interpret the QSPRs developed and to corroborate findings in the literature. Depending on resonance, the speciation state (through protonation and deprotonation) can heavily influence the oxidation rate constant, which was accurately predicted. The QSPRs can be applied in synthetic photochemistry and for estimating chemical fate from photolysis or advanced water treatment.

Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design

2012 ◽  
Vol 1425 ◽  
Author(s):  
Michael P. Krein ◽  
Bharath Natarajan ◽  
Linda S. Schadler ◽  
L. C. Brinson ◽  
Hua Deng ◽  
...  
Keyword(s):  
A Priori ◽  
Molecular Structures ◽  
Work Of Adhesion ◽  
Materials Informatics ◽  
Structure Property ◽  
Surface Tensions ◽  
Virtual Experiments ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Informatics Tools

ABSTRACTPolymer nanocomposites (PNC) are complex material systems in which the dominant length scales converge. Our approach to understanding nanocomposite tradespace uses Materials Quantitative Structure-Property Relationships (MQSPRs) to relate molecular structures to the polar and dispersive components of corresponding surface tensions. If the polar and dispersive components of surface tensions in the nanofiller and polymer could be determined a priori, then the propensity to aggregate and the change in polymer mobility near the particle could be predicted. Derived energetic parameters such as work of adhesion, work of spreading and the equilibrium wetting angle may then used as input to continuum mechanics approaches that have been shown able to predict the thermomechanical response of nanocomposites and that have been validated by experiment. The informatics approach developed in this work thus enables future in silico nanocomposite design by enabling virtual experiments to be performed on proposed nanocomposite compositions prior to fabrication and testing.

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