Studying of Atomic and Molecular Interaction Processes in Rarified Hypervelocity Expanding Flows by Methods of Emissive Spectroscopy

2004 ◽  
Author(s):  
George Karabadzhak
Keyword(s):  
Molecular Interaction ◽  
Interaction Processes

scholarly journals TarDict: RandomForestClassifier-based software predict Drug-Tartget interaction

2020 ◽  
Author(s):  
Peter T. Habib ◽  
Alsamman M. Alsamman ◽  
Sameh E. Hassanein ◽  
Aladdin Hamwieh
Keyword(s):  
Molecular Interaction ◽  
Training Data ◽  
Data Set ◽  
Interaction Processes ◽  
Similar Drug ◽  
Novel Drugs

AbstractPredicting the target of unknown or/and drugs under investigation from data of already identified drugs is very important not only for the understanding of various drug and molecular interaction processes but also for the development of novel drugs. Here we introduce TarDict, a RandomForestClassifier based-software predict the target pathway or protein based on SMILES of chemical. TarDict receives SMILES and returns a list of the possible similar drug, then export list to the user the target that drug contribute in. Training data set of 20442 entry and testing reveal %95 accuracy.

MOLECULAR INTERACTION THROUGH FREE VOLUME AND INTERNAL PRESSURE OF AQUEOUS AMINO ACIDS AT 308.15 K

2014 ◽  
Author(s):  
Amardeep Shende Hemant Baradkar Vilas Tabhane Amardeep Shende Hemant Baradkar Vilas Tabhane ◽  
Keyword(s):  
Amino Acids ◽  
Internal Pressure ◽  
Free Volume ◽  
Molecular Interaction

In silico Screening and Molecular Interaction Studies of Tetrahydrocannabinol and its Derivatives with Acetylcholine Binding Protein

2018 ◽  
Vol 12 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Priya P. Panigrahi ◽  
Ramit Singla ◽  
Ankush Bansal ◽  
Moacyr Comar Junior ◽  
Vikas Jaitak ◽  
...  
Keyword(s):  
Molecular Interaction ◽  
In Silico ◽  
Binding Protein ◽  
In Silico Screening ◽  
Acetylcholine Binding Protein ◽  
Interaction Studies

scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

Mixed matrix membranes with molecular-interaction-driven tunable free volumes for efficient bio-fuel recovery

2015 ◽  
Vol 3 (8) ◽  
pp. 4510-4521 ◽  
Author(s):  
Gongping Liu ◽  
Wei-Song Hung ◽  
Jie Shen ◽  
Qianqian Li ◽  
Yun-Hsuan Huang ◽  
...  
Keyword(s):  
Polymer Chain ◽  
Molecular Interactions ◽  
Molecular Interaction ◽  
Chain Conformation ◽  
Mixed Matrix Membranes ◽  
Mixed Matrix ◽  
Polymer Chain Conformation ◽  
Matrix Membranes

Molecular interactions were constructed to control polymer chain conformation to fabricate mixed matrix membranes with tunable free volumes, exhibiting simultaneously improved butanol permeability and selectivity.

Sign in / Sign up

Export Citation Format

Share Document