Molecular Fingerprints of Hydrophobicity at Aqueous Interfaces from Theory and Vibrational Spectroscopies

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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Integrative analysis reveals clinically relevant molecular fingerprints in pancreatic cancer

Molecular Therapy — Nucleic Acids ◽

10.1016/j.omtn.2021.06.015 ◽

2021 ◽

Author(s):

Libin Song ◽

Simin Qi ◽

Wei Hu ◽

Zhixiao Fang ◽

Dehua Yu ◽

...

Keyword(s):

Pancreatic Cancer ◽

Integrative Analysis ◽

Molecular Fingerprints

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Application of Vibrational Spectroscopies in the Qualitative Analysis of Gingival Crevicular Fluid and Periodontal Ligament during Orthodontic Tooth Movement

Journal of Clinical Medicine ◽

10.3390/jcm10071405 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1405

Author(s):

Fabrizia d’Apuzzo ◽

Ludovica Nucci ◽

Ines Delfino ◽

Marianna Portaccio ◽

Giuseppe Minervini ◽

...

Keyword(s):

Periodontal Ligament ◽

Tooth Movement ◽

Gingival Crevicular Fluid ◽

Orthodontic Tooth Movement ◽

High Sensitivity ◽

Data Analysis Procedure ◽

Vibrational Spectroscopies ◽

Composition Structure ◽

The Individual ◽

Crevicular Fluid

Optical vibrational techniques show a high potentiality in many biomedical fields for their characteristics of high sensitivity in revealing detailed information on composition, structure, and molecular interaction with reduced analysis time. In the last years, we have used these techniques for investigating gingival crevicular fluid (GCF) and periodontal ligament (PDL) during orthodontic tooth treatment. The analysis with Raman and infrared signals of GCF and PDL samples highlighted that different days of orthodontic force application causes modifications in the molecular secondary structure at specific wavenumbers related to the Amide I, Amide III, CH deformation, and CH3/CH2. In the present review, we report the most relevant results and a brief description of the experimental techniques and data analysis procedure in order to evidence that the vibrational spectroscopies could be a potential useful tool for an immediate monitoring of the individual patient’s response to the orthodontic tooth movement, aiming to more personalized treatment reducing any side effects.

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