Towards Predictive Modeling of Message-Passing Communication

2009 ◽  
Author(s):  
Verdi March ◽  
Vijayaraghavan Murali ◽  
Yong Meng Teo ◽  
Simon See ◽  
James T. Himer
Keyword(s):  
Predictive Modeling ◽  
Message Passing

Impact of Switching from Sulfonylureas to Dipeptidyl Peptidase-4 Inhibitors on Hypoglycemia Burden in the United States—A Predictive Modeling Approach

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 385-P
Author(s):  
JINAN LIU ◽  
YUEXIN TANG ◽  
HAKIMA HANNACHI ◽  
SAMUEL S. ENGEL ◽  
SWAPNIL RAJPATHAK
Keyword(s):  
United States ◽  
Predictive Modeling ◽  
The United States ◽  
Dipeptidyl Peptidase ◽  
Dipeptidyl Peptidase 4 ◽  
Modeling Approach ◽  
Dipeptidyl Peptidase 4 Inhibitors

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

2019 ◽  
Author(s):  
Michael Withnall ◽  
Edvard Lindelöf ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Message Passing ◽  
State Of The Art ◽  
A Priori ◽  
Model Performance ◽  
Learning Approaches ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Derived Properties ◽  
Memory Schemes ◽  
Hyperparameter Selection

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce <i>a priori</i> knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

Predictive modeling of sludge removal when drilling an inclined well

2018 ◽  
pp. 52-57
Author(s):  
P.A. Lykhin ◽  
◽  
E.V. Usov ◽  
V.N. Ulyanov ◽  
N.K. Kayurov ◽  
...  
Keyword(s):  
Predictive Modeling
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