scholarly journals Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

2016 ◽  
Vol 56 (7) ◽  
pp. 1332-1343 ◽  
Author(s):  
Sean Ekins ◽  
Alexander L. Perryman ◽  
Alex M. Clark ◽  
Robert C. Reynolds ◽  
Joel S. Freundlich
Keyword(s):  
Machine Learning ◽  
Mycobacterium Tuberculosis ◽  
Mouse Model ◽  
Small Molecules ◽  
Data Visualization ◽  
Learning Model ◽  
Model Analysis ◽  
Tuberculosis Infection ◽  
Mycobacterium Tuberculosis Infection ◽  
Machine Learning Model

scholarly journals Assessment of carbapenems in a mouse model of Mycobacterium tuberculosis infection

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0249841
Author(s):  
Ravindra Jadhav ◽  
Ricardo Gallardo-Macias ◽  
Gaurav Kumar ◽  
Samer S. Daher ◽  
Amit Kaushik ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Mouse Model ◽  
Small Molecules ◽  
Lessons Learned ◽  
Learning Approach ◽  
Mycobacterium Tuberculosis Infection ◽  
Pharmacokinetic Profiles ◽  
Machine Learning Approach ◽  
Selection Of

We present further study of a subset of carbapenems, arising from a previously reported machine learning approach, with regard to their mouse pharmacokinetic profiling and subsequent study in a mouse model of sub-acute Mycobacterium tuberculosis infection. Pharmacokinetic metrics for such small molecules were compared to those for meropenem and biapenem, resulting in the selection of two carbapenems to be assessed for their ability to reduce M. tuberculosis bacterial loads in the lungs of infected mice. The original syntheses of these two carbapenems were optimized to provide multigram quantities of each compound. One of the two experimental carbapenems, JSF-2204, exhibited efficacy equivalent to that of meropenem, while both were inferior to rifampin. The lessons learned in this study point toward the need to further enhance the pharmacokinetic profiles of experimental carbapenems to positively impact in vivo efficacy performance.

scholarly journals A Transferable Machine-Learning Model of the Electron Density

2018 ◽  
Author(s):  
Clemence Corminboeuf ◽  
Michele Certiotti ◽  
benjamin meyer ◽  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Charge Density ◽  
Small Molecules ◽  
Electron Density ◽  
Structure Data ◽  
Learning Model ◽  
Linear Scaling ◽  
Machine Learn ◽  
Machine Learning Model

<p>We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost.</p>

scholarly journals A Transferable Machine-Learning Model of the Electron Density

2018 ◽  
Author(s):  
Clemence Corminboeuf ◽  
Michele Certiotti ◽  
benjamin meyer ◽  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Charge Density ◽  
Small Molecules ◽  
Electron Density ◽  
Structure Data ◽  
Learning Model ◽  
Linear Scaling ◽  
Machine Learn ◽  
Machine Learning Model

<p>We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost.</p>

scholarly journals A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Natasha L. Patel-Murray ◽  
Miriam Adam ◽  
Nhan Huynh ◽  
Brook T. Wassie ◽  
Pamela Milani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Learning Model ◽  
Modes Of Action ◽  
Interpretable Machine Learning ◽  
Machine Learning Model
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