Molecular fingerprints based on Jacobi expansions of electron densities

2021 ◽  
Vol 140 (2) ◽  
Author(s):  
Rafael López ◽  
Frank Martínez ◽  
José Manuel García de la Vega
Keyword(s):  
Electron Densities ◽  
Molecular Fingerprints ◽  
Jacobi Expansions

EFFECT OF THE ION MOTION AT LOW ELECTRON DENSITIES ON THE PROFILES OF THE LINES 4471 Å AND 4922 Å OF HeI

1979 ◽  
Vol 40 (C7) ◽  
pp. C7-81-C7-82
Author(s):  
C. Fleurier ◽  
G. Coulaud ◽  
J. Chapelle
Keyword(s):  
Electron Densities ◽  
Ion Motion

scholarly journals Accuracy of electron densities obtained via Koopmans-compliant hybrid functionals

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
A. R. Elmaslmane ◽  
J. Wetherell ◽  
M. J. P. Hodgson ◽  
K. P. McKenna ◽  
R. W. Godby
Keyword(s):  
Electron Densities ◽  
Hybrid Functionals

Visualization of two-body electron densities and wave functions of magnetic molecules

1999 ◽  
Vol 75 (4-5) ◽  
pp. 645-654 ◽  
Author(s):  
D. Yamaki ◽  
Y. Kitagawa ◽  
H. Nagao ◽  
M. Nakano ◽  
Y. Yoshioka ◽  
...  
Keyword(s):  
Wave Functions ◽  
Electron Densities ◽  
Magnetic Molecules

Microdefects and electron densities in NiTi shape memory alloys studied by positron annihilation

2006 ◽  
Vol 16 (6) ◽  
pp. 1259-1262 ◽  
Author(s):  
Yi-feng HU ◽  
Wen DENG ◽  
WEN-bo HAO ◽  
Li YUE ◽  
Le HUANG ◽  
...  
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Positron Annihilation ◽  
Electron Densities ◽  
Niti Shape Memory Alloys

λ21-cm Interstellar HI Profiles, Critical Ionization Velocities, and Derived Electron Densities

2021 ◽  
pp. 1-10
Author(s):  
Gerrit L. Verschuur ◽  
Joan T. Schmelz ◽  
Mahboubeh Asgari-Targhi
Keyword(s):  
Electron Densities

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

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