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Yes, heating some vape flavour chemicals can plausibly create new compounds, but the 2024 AI study did not detect a complete toxic mixture in human lungs, prove that every predicted product forms in a device, or show that vaping causes cancer. It was a computational screening study: a graph-convolutional neural network proposed possible first-stage breakdown reactions for 180 flavour chemicals, then investigators compared the predicted products’ molecular weights with existing mass-spectrometry fragments. The result is a list of candidates for laboratory testing, not a clinical diagnosis or a universal emissions profile.
What the 2024 AI study actually asked
Akihiro Kishimoto, Dan Wu and Donal F. O’Shea reported the work in Scientific Reports in 2024. Their question was chemical: when a flavour molecule is exposed to intense heat, what smaller molecules might it form in an initial pyrolysis step?
The team assembled 180 flavour chemicals reported in e-liquids. The model was a Weisfeiler–Lehman graph-convolutional neural network, which represents a molecule as connected atoms and bonds and learns reaction patterns from examples. Its reaction-training data came from US patent literature. To reduce data leakage, the flavour molecules used in the vape analysis were removed from the training set.
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For each parent flavour, the model generated possible unimolecular pyrolysis transformations—reactions involving one molecule at a time. It predicted 7,307 possible products. That number is an output of the model, not a count of chemicals collected from an aerosol.
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The researchers then used a second, indirect check. They compared each predicted product’s molecular weight with electron-impact mass-spectrometry (EI-MS) fragment data in the National Institute of Standards and Technology database for the corresponding parent flavour. A shared molecular weight can make a prediction worth testing, because both energetic bond breaking in a mass spectrometer and heat-driven bond breaking can produce related fragments. However, molecular weight alone does not identify a unique structure. It also does not establish that the product forms at a vape coil’s temperature or survives long enough to be inhaled.
Key numbers and what they represent
| Reported result | What it means | What it does not mean |
|---|---|---|
| 180 flavour chemicals | The representative parent molecules analysed by the model. | Not every flavour used in every e-liquid. |
| 7,307 predicted products | Possible first-stage pyrolysis products generated computationally. | Not 7,307 chemicals measured in vape aerosol or lungs. |
| 1,169 molecular-weight matches | Predictions whose molecular weights matched EI-MS fragment masses for a parent flavour. | Not direct structural identification or proof of formation during vaping. |
| 127 acute-toxic, 153 health-hazard and 225 irritant classifications | Global Harmonized System (GHS) hazard categories assigned to matched predictions in an IBM Research summary. | Not aerosol concentrations, exposure doses, diagnoses or observed illnesses. |
Why the hazard classifications are easy to misread
The IBM summary’s GHS counts classify predicted molecules according to hazard information available for those chemical identities. A classification such as “acute toxic” describes a property category under the GHS framework; it does not say how much of that substance is present in a particular puff, whether a user inhales it, or whether the dose reaches a harmful level.
The categories can overlap: one candidate may qualify for more than one hazard class. They also do not indicate that the model found 127 separate toxic substances in a device. The counts are therefore useful for prioritising which predictions deserve analytical and toxicological testing, but they cannot be converted into a risk percentage for vaping.
Did AI prove that vape flavours cause cancer?
No. The study did not follow vapers, measure cancer, expose animals or cells to the predicted products, or establish a dose–response relationship. It identified plausible chemical pathways and flagged candidates with existing hazard classifications. Showing cancer causation would require a chain of evidence linking a defined exposure to biological changes and then to increased disease incidence in appropriately designed studies. The 2024 analysis does not provide those steps.
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It is also incorrect to say that AI “found the cocktail in users’ lungs.” No lung samples were analysed. No complete aerosol mixture was collected. The model did not calculate how much of any candidate would be produced per puff or deposited in respiratory tissue.
How heating can change a flavour molecule
Pyrolysis is chemical decomposition caused by heat, generally in conditions where ordinary combustion is not the main process. A vape coil can create steep, localised temperature gradients, and the liquid film on the coil may experience different conditions from the air that leaves the device. Bond cleavage can generate smaller aldehydes, ketones, alcohols, acids or aromatic compounds, depending on the parent structure and surroundings.
The study examined only the first phase of possible pyrolysis and omitted combustion products. Real devices add variables that the model did not reproduce: applied power, coil material and age, liquid flow, puff duration, airflow, temperature control, and the amount of liquid reaching the coil. Two devices using the same flavour can therefore produce different emissions. A dry or overheated coil may create a different chemistry from a well-supplied coil, while repeated heating can alter the liquid itself.
O’Shea explained the motivation in a 2024 Chemistry World interview: “As the flavours being used in vapes have never been clinically tested for heating to high temperatures with lung inhalation, it is important to quickly get an understanding of the cocktail of chemicals that vape-users’ lungs are being exposed to.” The statement describes an evidence gap, not a finding that a particular cocktail has already been measured in people.
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What the 2026 aerosol experiment adds
A 2026 paper in Chemical Research in Toxicology provides a different kind of evidence: direct laboratory measurement of emissions. Investigators tested eight representative flavouring chemicals across several flavour classes. They varied device power (50 and 90 watts), propylene-glycol/vegetable-glycerin (PG/VG) ratios of 80:20, 50:50 and 20:80, and flavour concentrations of 1 and 5 milligrams per millilitre.
Under those tested conditions, flavoured liquids produced more carbonyl emissions than unflavoured controls. Terpene-based flavours showed the strongest reported effects, with formaldehyde increasing by up to twofold and acrolein by up to eightfold relative to the unflavoured comparison. Those are condition-specific laboratory results, not a universal multiplier for every e-liquid, coil or puffing pattern.
The experiment demonstrates why formulation and operating conditions matter. Changing power, PG/VG ratio or flavour concentration changed the emissions profile. It does not establish long-term clinical outcomes, and it does not validate every product that was not tested. Its value is that it measures chemicals in generated aerosol, whereas the 2024 model mainly prioritised candidates for measurement.
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| Evidence type | What is measured | Inference that is justified |
|---|---|---|
| Neural-network prediction | Possible reaction products from molecular structures. | A plausible pathway can be proposed and ranked for testing. |
| EI-MS fragment-mass comparison | Whether a predicted product’s molecular weight matches a parent molecule’s fragment mass. | The candidate may merit follow-up; mass matching does not uniquely identify structure. |
| Laboratory aerosol measurement | Chemical concentrations emitted under specified device and liquid conditions. | The measured setup generated those emissions; results may change with other conditions. |
| Cell or animal toxicology | Biological responses to a defined chemical and dose. | Evidence of toxicity in that model, not automatically a human disease effect. |
| Human clinical or epidemiological evidence | Exposure, biological markers and health outcomes in people. | Stronger evidence about real-world risk, subject to study design and confounding. |
Why food-use approval does not settle inhalation safety
A 2025 review by Belgium’s Superior Health Council says that food-safety evaluations do not address inhalation toxicity. A flavouring can be acceptable for ingestion at an intended food dose yet behave differently when heated and delivered directly to the respiratory tract. The lungs have different barriers, enzymes and clearance mechanisms from the digestive system.
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The review characterises clinical evidence on inhaled flavourings as limited. Much of the available toxicity literature is in vitro, with comparatively little in vivo work. It also says that reaction products created by heating or by mixing ingredients are not fully known, and that their effects remain uncertain. “Food grade” therefore describes an approved use context; it is not a certification that the substance is safe to inhale after heating.
What the studies can and cannot tell someone who vapes
The evidence supports a cautious interpretation rather than a simple safe/unsafe label. Flavour chemistry can change under heat, and measured carbonyl emissions vary with the liquid and device settings. At the same time, the AI study does not supply a personal exposure estimate or prove disease.
- Do not treat a flavour name as a chemical safety test. A single commercial name can represent a mixture, and the listed ingredients may not describe all heat-generated products.
- Do not convert a GHS category into a risk calculation without concentration, inhaled dose, frequency and duration.
- Do not assume that an unflavoured liquid has zero emissions. The 2026 experiment used unflavoured controls for comparison; it did not establish that every unflavoured formulation is harmless.
- Do not infer that lower device power always eliminates hazardous chemistry. Power is one variable among coil condition, liquid supply, PG/VG ratio, concentration and puff behaviour.
- Be sceptical of consumer “vape safety” tests that claim to certify an entire product without specifying the exact liquid, device, power and compounds measured. The cited studies do not validate a general-purpose home test kit.
Anyone experiencing breathing difficulty, chest pain, persistent cough or other concerning symptoms should seek medical care. A clinician can address symptoms and exposure history; neither the AI model nor a product label can diagnose an individual.
Important limitations and unanswered questions
The 180-molecule set is not a census
The analysed chemicals were representative examples reported in e-liquids, not a complete inventory of all flavourings, contaminants, sweeteners or additives. A flavour absent from the set was outside the model’s scope.
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Only an initial decomposition stage was modelled
Later-stage reactions, interactions between multiple ingredients and combustion chemistry were not included. The predicted list should therefore be viewed as a first screening layer rather than a complete emissions catalogue.
A mass match is not a molecular identification
Different structures can share a molecular weight. Confirming a candidate would require analytical methods such as retention-time comparisons and authentic standards, followed by measurements under controlled device conditions.
No exposure or dose was established
The 2024 work did not report concentration per puff, inhaled dose, lung deposition, metabolism or clearance. Without those quantities, hazard classifications cannot be translated directly into an individual’s risk.
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Long-term human outcomes remain a separate question
The 2025 public-health review notes limited clinical evidence for inhaled flavourings. The 2026 aerosol experiment measured emissions but did not follow users or establish chronic disease outcomes. Those gaps are why the findings should inform further testing rather than be presented as proof of a specific illness.
What would turn a prediction into stronger evidence?
- Confirm the chemistry. Generate the candidate under controlled coil temperatures and identify its structure with validated analytical standards, not molecular weight alone.
- Measure realistic emissions. Report concentration per puff across defined power, airflow, puff duration, coil age, PG/VG ratio and flavour concentration.
- Characterise exposure. Determine how much reaches the airway, how long it persists and what metabolites form.
- Test biological effects. Use appropriate cell, tissue and animal models with exposure levels tied to measured aerosol concentrations.
- Study people. Well-designed clinical and epidemiological studies would be needed to connect sustained exposure with respiratory or cardiovascular outcomes while accounting for smoking and other confounders.
The 2024 AI analysis is useful at the first step: it narrows a very large chemical search space. The 2026 measurements move some questions toward the second step. Neither substitutes for the exposure and health evidence required to claim that a particular flavour causes cancer or another disease.
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