Aging is a huge health, societal, and economical burden that is marked by a decline in an organism's
physiological activities.
Many diseases, including malignancies, cardiovascular diseases, and neurological diseases, are linked
to advancing age.
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Though chronological ageing cannot be reversed, artificial intelligence may soon assist us
in slowing biological ageing, which is impacted by lifestyle, behaviour, and other factors
and is a more accurate predictor of health and mortality.
We can easily guess someone's age by looking at them, hearing them speak, or even smelling
them.
These are all symptoms of biological ageing.
Even a layperson's eye, let alone a doctor's, may be able to detect a person's health status
if they appear older than their chronological age.
AI's ability to absorb and analyse massive volumes of data makes it a valuable ally in
the quest to not just discover ageing indicators before they manifest biologically, but also
to help people live longer, healthier lives.
Through patterns in medical data, photographs, and other sources, AI can recognise trends
and determine casualties.
AI algorithms evaluated daily images of mice to extract probable ageing markers and build
lifetime management methods in a project with mice.
The same visual biomarker learning mechanism can subsequently be applied to other species,
including humans.
Beyond picture analysis tools, AI can detect biomarkers by identifying recurring patterns
in human ageing.
AI can detect ageing trends in different populations by sifting through large amounts of data from
various blood tests, retinal scans, muscle analysis, and other sources, or by comparing
human data to that of other species.
AI can offer health solutions in addition to recognising prospective difficulties by
studying the effects of therapeutic treatments, preventive remedies, different lifestyles,
and more.
On a deeper level, understanding how proteins and cells respond favourably or adversely
to therapies allows AI to aid in the efficient development of medicine.
Deep learning's capability could soon assist us in alleviating the discomforts of biological
ageing.
A team of Surrey chemists developed a machine learning model based on data from the DrugAge
database to predict whether a compound can extend the life of Caenorhabditis elegans,
a translucent worm with a metabolism similar to humans, according to a paper published
in Nature Communication's Scientific Reports.
Due of the worm's short lifespan, the researchers were able to observe the effects of the chemical
compounds.
The AI identified three chemicals that have an 80% likelihood of extending the lifespan
of elegans: flavonoids, which are anti-oxidant pigments found in plants that enhance cardiovascular
health, fatty acids like omega 3, and organooxygens, which include carbon to oxygen connections
and include alcohol.
Sofia Kapsiani, one of the study's co-authors and a final-year undergraduate student at
the University of Surrey, said: "In modern medicine, ageing is increasingly being recognised
as a series of diseases, and we may use digital technologies like AI to assist slow down or
prevent ageing and age-related diseases."
Our research reveals AI's groundbreaking ability to aid in the identification of anti-aging
chemicals."
"This research highlights the power and promise of AI, which is a specialisation of the University
of Surrey, to generate huge advances in human health," stated Dr. Brendan Howlin, main author
of the study and Senior Lecturer in Computational Chemistry at the University of Surrey.
Many disorders, such as cancer, cardiovascular disease, and neurological diseases, are linked
to ageing.
An emerging study topic is pharmaceutical interventions that slow down the ageing process
and delay the onset of age-related illnesses.
The goal of this work was to create a machine learning model based on the DrugAge database's
data to predict whether a chemical substance would lengthen the lifetime of Caenorhabditis
worms.
The random forest approach was used to create five predictive models with molecular fingerprints
and/or molecular descriptors as features.
For categorising the substances in the test set, the best performing classifier, created
using molecular descriptors, achieved an area under the curve score of 0.815.
The random forest algorithm's Gini significance measure was used to rank the model's features.
Descriptors relating to atom and bond counts, topological and partial charge qualities were
among the top 30 attributes.
In an external database of 1738 small molecules, the model was used to predict the class of
compounds.
The screening database's chemical compounds with a predictive probability of 0.80 for
enhancing Caenorhabditis worm longevity were divided into flavonoids, fatty acids and conjugates,
and organooxygen compounds.
Interventions that target the cellular and molecular processes of ageing have the potential
to prevent and delay age-related diseases.
Using food limitations, genetic alterations, and pharmaceutical interventions, several
ageing studies have revealed interventions that prolong the lifetime of model species
ranging from nematodes and fruit flies to rodents.
In the multicellular species Caenorhabditis elegans, Lee et al. revealed the first evidence
that long-term nutritional deprivation can promote longevity.
Harrison et al. discovered that treating mice with rapamycin, an inhibitor of the mTOR system,
increased the median and maximum longevity of the mice a few years later.
Selman et al. also discovered that genetic deletion of S6 protein kinase 1, a component
of the mTOR signalling system, extended the lifespan of mice and protected them from age-related
diseases.
To find pharmacological classes relevant to C. elegans ageing, Ye et al. created a pharmacological
network.
Resistance to oxidative stress and lifespan extension were shown to be grouped in a few
pharmacological classes, the majority of which were connected to intercellular signalling.
Putin et al. also created a deep learning neural network that could predict human chronological
age based on a simple blood test.
Albumin, glucose, alkaline phosphatase, urea, and erythrocytes were found to be the top
five most important blood markers for establishing chronological age in humans.
Mamoshina et al. also built a deep learning-based haematological ageing clock with millions
of patients using blood samples from Canadian, South Korean, and Eastern European populations.
The findings revealed that population-specific ageing clocks were more accurate than generic
ageing clocks in predicting chronological age and defining biological age.
Based on the data from the DrugAge database, Barardo et al. created a random forest model
to predict if a chemical will increase the lifespan of C. elegans.
Molecular descriptors and gene ontology terms were employed to construct the model.
The random forest's feature importance measure was used to choose features.
The best-performing model was used to predict the class of compounds in the DGIdb database,
with an AUC score of 0.80.
This study expands on Barardo et alwork .'s by looking at the usage of the DrugAge database
to forecast molecules with anti-aging capabilities.
The random forest technique was used to determine whether a substance will prolong the lifespan
of C. elegans.
Based on the data from the DrugAge database released by Barardo et al., five predictive
models were built, each using distinct descriptor categories.
Molecular fingerprints and/or molecular descriptors calculated from the structure of the substances
in the DrugAge database were used to create the models.
Variance and mutual information-based approaches were used to choose features.
To our knowledge, this is the first time molecular fingerprints have been used to develop machine
learning models based on DrugAge database entries.
The best model was used to predict the class of chemicals in an external database, which
included 1738 small molecules retrieved from the DrugBank database.
Random forest is a supervised machine learning technique that consists of an ensemble of
decision trees, each of which is trained separately using a random subset of data.
Because it is resistant to overfitting on high-dimensional datasets with limited sample
sizes, the random forest model is commonly used in chemo- and bioinformatics activities.
The chemical descriptors used in quantitative structure–activity relationship models can
have a big impact on their quality and predictions.
Descriptors are computer-interpretable digital or numerical representations of chemical information
of molecules that are ideal for model creation.
The Molecular Operating Environment software was used to construct 2D and 3D molecular
descriptors in this investigation.
2D descriptors are derived from a molecule's 2D structure and give data on its structural,
topological, and physicochemical features.
3D descriptors, on the other hand, are derived from a chemical compound's 3D structure and
include electronic parameters, quantum–chemical descriptors, and surface:volume descriptors.
Molecular fingerprints use binary vectors to indicate a molecule's structure, with 1
indicating the presence of a feature or structural group and 0 indicating its absence.
The RDKit Python environment was used to generate extended-connectivity fingerprints of 1024
and 2048 bits, as well as RDKit topological fingerprints of 2048 bits.
Finally, the use of molecular descriptors in conjunction with ECFPs was investigated.
To choose a subset of significant variables for predicting the anti-ageing capabilities
of the compounds in the dataset, researchers used variance and mutual information filter-based
approaches.
Only the training set, which contained 80% of the chemicals in the dataset, was used
for this.
The number of variables employed by each model was reduced as a result of feature selection,
lowering the cost of computational calculations.
For identifying the chemicals in the test set, the feature subset with the greatest
AUC score in 10-fold cross-validation was chosen.
When two feature subsets with the same AUC score were employed, the subset with the lowest
standard deviation was chosen.
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