scholarly journals Fuzzy ARTMAP and Back-Propagation Neural Networks Based Quantitative Structure−Property Relationships (QSPRs) for Octanol−Water Partition Coefficient of Organic Compounds

2002 ◽  
Vol 42 (2) ◽  
pp. 162-183 ◽  
Author(s):  
Denise Yaffe ◽  
Yoram Cohen ◽  
Gabriela Espinosa ◽  
Alex Arenas ◽  
Francesc Giralt
Keyword(s):  
Neural Networks ◽  
Partition Coefficient ◽  
Organic Compounds ◽  
Back Propagation ◽  
Fuzzy Artmap ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Water Partition Coefficient ◽  
Quantitative Structure Property Relationships

scholarly journals Predicting permeability via statistical learning on higher-order microstructural information

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Magnus Röding ◽  
Zheng Ma ◽  
Salvatore Torquato
Keyword(s):  
Neural Networks ◽  
Physical Properties ◽  
Specific Surface ◽  
Correlation Functions ◽  
Quantitative Structure ◽  
Structure Property ◽  
Complex Materials ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Point Correlation

Abstract Quantitative structure–property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure–property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

scholarly journals Artificial Intelligence in Drug Design

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2520 ◽  
Author(s):  
Gerhard Hessler ◽  
Karl-Heinz Baringhaus
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Drug Discovery ◽  
De Novo ◽  
Biologically Active ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Near Future

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

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