How Algorithms Are Creating—and Combating—New Psychoactive Substances
The same artificial intelligence that can design life-saving drugs is now being used to create dangerous new psychoactive substances, launching a high-stakes digital battle between AI designers and AI detectives.
In laboratories worldwide, a silent revolution is transforming how we discover and design chemical compounds. While much attention has focused on AI's potential to create new medicines, a parallel development is occurring in the shadows: the emergence of AI-designed psychoactive substances. Over the past decade, more than a thousand new psychoactive substances (NPS) have flooded global markets, creating a public health crisis and challenging traditional drug control systems. Now, artificial intelligence is accelerating this trend—while also providing powerful new tools to combat it.
New psychoactive substances, often called "designer drugs" or "research chemicals," are synthetic compounds engineered to mimic the effects of traditional illicit drugs while bypassing existing drug laws. By the end of 2021, over 1,100 NPS had been documented worldwide, with approximately two new compounds reported each week in Europe alone 1 . These substances span diverse chemical classes including synthetic cannabinoids, cathinones, opioids, and phenethylamines, most of which have never undergone formal safety testing and have been linked to severe toxic effects and fatalities 1 .
The traditional regulatory approach has struggled to keep pace with this rapid proliferation. When authorities ban specific compounds, clandestine chemists simply modify molecular structures to create new, legal analogues. This "cat-and-mouse" cycle has persisted for years, but the game is changing dramatically with the introduction of artificial intelligence 1 .
New Psychoactive Substances documented worldwide by 2021
At the forefront of AI-driven NPS design are generative models—deep learning systems that can create novel molecular structures with desired psychoactive properties. These models learn from databases of known psychoactive compounds to understand the chemical "patterns" associated with specific effects, then generate new variations that fit these patterns 1 .
One of the most advanced approaches is the STNGS framework (Scaffold and Transformer-Based NPS Generation and Screening), which combines a scaffold-based molecular generative model with a multi-factor scoring system. Instead of creating random new molecules, STNGS focuses on known core scaffolds common in NPS and uses a transformer-based neural network to generate analogues around those scaffolds. The generated molecules are then filtered and ranked by an ensemble of criteria including synthetic accessibility, novelty, and predicted receptor activity 1 .
Quantitative structure-activity relationship (QSAR) modeling represents another powerful AI tool in the NPS arena. These machine learning models predict biological activities—such as binding affinity to neurological receptors—from chemical structure descriptors. For synthetic cannabinoids, researchers have developed QSAR models that accurately predict how strongly new analogues will bind to the CB1 receptor in the brain, which is responsible for cannabis-like psychoactive effects 1 .
These models essentially act as early warning systems: when a new cannabinoid analogue emerges, its structure can be input to the model to estimate whether it will be highly potent and dangerous, helping regulators prioritize their response 1 .
Gather structural data on known psychoactive compounds from scientific literature and forensic reports.
Train generative AI models (like SeqGAN and MolGPT) on known psychoactive structures to learn chemical patterns.
AI systems generate thousands of novel molecular structures with desired psychoactive properties.
Filter and rank generated molecules based on synthetic accessibility, novelty, and predicted activity.
Chemically synthesize selected candidates and validate their properties in the laboratory.
In a groundbreaking demonstration of AI's capabilities, researchers recently showed how deep learning could generate previously unknown fentanyl analogues—a particularly concerning development given the ongoing opioid crisis 1 .
Remarkably, all ten AI-generated compounds turned out to be previously unreported fentanyl analogues 1 . This demonstrated for the first time that deep learning could generate chemically diverse analogues of fentanyl that had not yet been identified by law enforcement—a finding with significant implications for both forensic science and public health.
Validation Rate: 100% (All proposed structures were synthetically feasible)
| Metric | Result | Significance |
|---|---|---|
| Total molecules generated | 11,000+ | Demonstrates AI's ability to explore vast chemical space |
| Previously unknown analogues identified | 10 out of 10 | Shows AI can create truly novel compounds |
| Chemical diversity | High | Indicates AI can move beyond simple analogues |
| Validation rate | 100% | All proposed structures were synthetically feasible |
| Tool Category | Specific Examples | Function in NPS Research |
|---|---|---|
| Generative Models | DarkNPS, STNGS, SeqGAN, MolGPT | Create novel molecular structures with desired psychoactive properties |
| Predictive Models | QSAR models, Random Forests, Neural Networks | Forecast biological activity, toxicity, and physicochemical properties |
| Data Resources | OMol25, Darknet market data, Forensic databases | Provide training data for AI models and reference for identification |
| Simulation Tools | Density Functional Theory (DFT), Molecular dynamics | Predict molecular properties and behavior before synthesis |
| Analytical AI | Spectral prediction algorithms, Pattern recognition | Identify unknown substances through mass spectrometry and other methods |
The Open Molecules 2025 (OMol25) dataset deserves special mention as a foundational resource. This unprecedented collection of more than 100 million 3D molecular snapshots—whose properties were calculated with density functional theory—provides an extensive training ground for AI models working in molecular design 5 . While originally developed for legitimate scientific research, such resources could potentially be misapplied to NPS development.
While AI can potentially be misused for designing NPS, the same technology is revolutionizing how we detect and control these substances.
Traditional forensic workflows for identifying NPS rely on library matching against known reference spectra. This approach fails when novel compounds emerge that aren't in existing databases. AI is solving this problem through spectral prediction algorithms that can predict the mass spectra of unknown compounds based on their molecular structures 1 .
The DarkNPS approach, one of the first deep generative models for NPS, was trained on a database of known high-resolution mass spectra of NPS to learn the distribution of structural features in these compounds. By sampling this model, researchers generated a vast library of plausible NPS-like structures that could aid in elucidating the identity of unknown designer drugs. Notably, a majority of recently discovered NPSs fell within the chemical space covered by the model's generated compounds 1 .
AI tools are helping regulatory bodies stay ahead of emerging threats by predicting which new analogues are likely to appear on the market. By analyzing patterns in chemical space and online drug markets, AI systems can identify vulnerable molecular templates that are likely to be modified next, allowing proactive rather than reactive control measures 1 .
| Application Area | AI Technology | Impact |
|---|---|---|
| Unknown compound identification | Spectral prediction algorithms | Reduces dependence on reference standards and libraries |
| Risk assessment | QSAR models | Flags particularly hazardous new analogues for priority control |
| Market monitoring | Natural language processing, Pattern recognition | Tracks emerging compounds through online marketplaces and forums |
| Regulatory prioritization | Predictive analytics | Helps authorities focus resources on highest-risk compounds |
The emergence of AI-designed psychoactive substances raises profound legal and ethical questions that current regulatory frameworks are poorly equipped to handle 1 .
The European Union and United States both face the challenge of adapting drug control systems designed for a pre-AI era. Most laws ban specific compounds or use analogue provisions that require chemical similarity to known controlled substances. However, AI can generate chemically distant analogues that may fall outside these definitions while maintaining psychoactive effects 1 .
There's also the concerning possibility of automated drug discovery systems that could continuously generate, evaluate, and even synthesize new psychoactive compounds with minimal human intervention. Such systems could potentially flood markets with novel substances faster than regulators can respond.
We stand at a crossroads where the same artificial intelligence tools that promise to revolutionize medicine also threaten to destabilize drug control systems worldwide. The development of AI methods for new psychoactive substance design represents a powerful dual-use technology—with applications for both public health and public harm.
The genie is out of the bottle: AI-driven molecular design is here to stay. The critical question now is how we will harness these powerful technologies for protection rather than harm, ensuring that our AI detectives remain at least one step ahead of the AI designers in this high-stakes molecular arms race.
What seems certain is that the future of drug control will be increasingly digital, computational, and algorithmic—a world where chemical innovation and chemical regulation are both accelerated by artificial intelligence.