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AlphaFold Uncovers Thousands of Potential Psychedelic Proteins

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Researchers recently used an artificial intelligence (AI) tool developed by London-based artificial intelligence research laboratory Deepmind Technologies Limited to identify thousands of potential psychedelic proteins. Called AlphaFold, the artificial intelligence protein-structure prediction tool found hundreds of thousands of new potential psychedelic molecules that could be instrumental in the development of novel antidepressants.

The research is a testament to AlphaFold’s ability to easily provide predictions that have applications in everything from experimentally derived protein structure to drug discovery in a fraction of the time such endeavors would have previously taken. AlphaFold’s public database currently has structure predictions for almost every known protein and has the potential to significantly expedite efforts to discover and improve novel therapies using the protein structure of molecules.

The discovery of so many new potential psychedelics could help researchers develop more effective psychedelic-based mental-health treatments. A growing body of research has already found that numerous psychedelics can offer long-term relief against a myriad of mental disorders, especially when that treatment is paired with traditional talk therapy.

University of Uppsala computational chemist Jens Carlsson says the protein-structure prediction tool is an “absolute revolution” and notes that it should be useful in new drug design if it has a good structure. However, some scientists doubt whether AlphaFold’s psychedelic molecule predictions could act as gold-standard experimental models in the race to discover new psychedelic drugs.

Brian Shoichet, a University of California pharmaceutical chemist, says efforts to apply the AI tool in new drug discoveries have faced “considerable skepticism.” He notes that while there is plenty of hype surrounding the AI tool, claiming that any tool will “revolutionize drug discovery” is sure to attract some skepticism.

Schoichet outlined more than 10 studies that have found the AI tool’s predictions aren’t more useful than protein structures collected using experimental methods such as X-ray crystallography when using protein-ligand docking to model potential new drugs. Protein-ligand docking is typically applied in the initial stages of drug discovery and involves modeling the interactions of hundreds of millions to even billions of chemicals with critical regions of the target protein to find compounds that alter its activity.

Past studies have found that when models use predicted structures from AlphaFold, their ability to single out drugs that are already known to impact the protein’s activity is poor. A research team under the lead of Schoichet and University of North Carolina structural biologist Bryan Roth ran a similar study and came to the same conclusions.

Despite acknowledging that the AI tool’s predictions weren’t universally useful, Schoichet noted that around one-third of cases using an AlphaFold structure could result in accelerating a project and advance research by several years.

It remains to be seen whether major psychedelics drug-development companies such as Compass Pathways PLC (NASDAQ: CMPS) will find a way to add AlphaFold’s models into their workflows in order to assess how doing so will impact their processes and costs.

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