Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism

2021 ◽  
Vol 23 (35) ◽  
pp. 19781-19789
Author(s):  
Tom Vermeyen ◽  
Jure Brence ◽  
Robin Van Echelpoel ◽  
Roy Aerts ◽  
Guillaume Acke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Circular Dichroism ◽  
Absolute Configuration ◽  
Vibrational Circular Dichroism ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Absolute ◽  
Circular Dichroïsm ◽  
Machine Learning Models

The capabilities of machine learning models to extract the absolute configuration of a series of compounds from their vibrational circular dichroism spectra have been demonstrated. The important spectral areas are identified.

An investigation of the absolute configuration of the potent histamine H3 receptor antagonist GT-2331 using vibrational circular dichroism

Chirality ◽  
2007 ◽  
Vol 19 (9) ◽  
pp. 731-740 ◽  
Author(s):  
Douglas J. Minick ◽  
Royston C.B. Copley ◽  
Jerzy R. Szewczyk ◽  
Randy D. Rutkowske ◽  
Luke A. Miller
Keyword(s):  
Circular Dichroism ◽  
Receptor Antagonist ◽  
Absolute Configuration ◽  
Vibrational Circular Dichroism ◽  
Histamine H3 Receptor ◽  
H3 Receptor ◽  
The Absolute ◽  
Circular Dichroïsm ◽  
Histamine H3

Short Synthesis of Berkeleyamide D and Determination of the Absolute Configuration by the Vibrational Circular Dichroism Exciton Chirality Method

2014 ◽  
Vol 16 (5) ◽  
pp. 1386-1389 ◽  
Author(s):  
Kenta Komori ◽  
Tohru Taniguchi ◽  
Shoma Mizutani ◽  
Kenji Monde ◽  
Kouji Kuramochi ◽  
...  
Keyword(s):  
Circular Dichroism ◽  
Absolute Configuration ◽  
Vibrational Circular Dichroism ◽  
Short Synthesis ◽  
The Absolute ◽  
Circular Dichroïsm

Determination of the absolute configuration and solution conformation of the antifungal agents ketoconazole, itraconazole, and miconazole with vibrational circular dichroism

Chirality ◽  
2005 ◽  
Vol 17 (S1) ◽  
pp. S101-S108 ◽  
Author(s):  
David Dunmire ◽  
Teresa B. Freedman ◽  
Laurence A. Nafie ◽  
Christine Aeschlimann ◽  
John G. Gerber ◽  
...  
Keyword(s):  
Circular Dichroism ◽  
Absolute Configuration ◽  
Antifungal Agents ◽  
Vibrational Circular Dichroism ◽  
Solution Conformation ◽  
The Absolute ◽  
Circular Dichroïsm

The absolute configuration of angular 3′-acyloxypyranocoumarins by vibrational circular dichroism exciton chirality

2014 ◽  
Vol 25 (20-21) ◽  
pp. 1418-1423 ◽  
Author(s):  
Abigail I. Buendía-Trujillo ◽  
J. Martín Torrres-Valencia ◽  
Pedro Joseph-Nathan ◽  
Eleuterio Burgueño-Tapia
Keyword(s):  
Circular Dichroism ◽  
Absolute Configuration ◽  
Vibrational Circular Dichroism ◽  
The Absolute ◽  
Circular Dichroïsm

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia

2019 ◽  
pp. 1-11 ◽  
Author(s):  
David Chen ◽  
Gaurav Goyal ◽  
Ronald S. Go ◽  
Sameer A. Parikh ◽  
Che G. Ngufor
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Unsupervised Clustering ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Continuous Output ◽  
Time To First Treatment ◽  
Machine Learning Models

PURPOSE Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with CLL. PATIENTS AND METHODS A total of 737 treatment-naïve patients with CLL diagnosed at Mayo Clinic were included in this study. We compared predictive abilities for two survival models (Cox proportional hazards and random survival forest) and four classification methods (logistic regression, support vector machines, random forest, and gradient boosting machine). Probability of treatment was then stratified. RESULTS Machine learning methods did not yield significantly more accurate predictions of time to first treatment. However, automated risk stratification provided by clustering was able to better differentiate patients who were at risk for treatment within 1 year than models developed using standard survival analysis techniques. CONCLUSION Clustering the posterior probabilities of machine learning models provides a way to better interpret machine learning models.

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