We recognize that natural and classical psychedelics like psilocybin or DMT were uncovered and brought about by brave psychedelic research pioneers, drawing from the knowledge of indigenous communities worldwide.
We aim to design and develop new psychedelic-inspired drugs with superior properties.
By identifying potential candidates in vast chemical space and then optimizing their safety and efficacy for specific indications, we can unlock the full potential of psychedelics and maximize the amount of people who could benefit from them.
There are more potential drug-like compounds than there are atoms in the observable universe. Then there are tens of thousands of targets in the human body where these small molecules can bind.
It is then no surprise that finding a compound with the right binding profile and optimal properties by standard screening requires an enormous amount of money and time.
Discovery and optimisation processes that traditionally take years can be compressed into days.
By utilizing a range of different AI-driven methods, we are able to efficiently navigate the immense chemical space while searching for drug candidates.
For example, we can build a model that automatically generates novel compounds with a pre-defined distribution of properties. We can then extract insights from millions of experimental affinity measurements to predict the binding of these molecules to many proteins at the same time.
We also employ many models to predict various ADMET properties such as bioavailability, CNS penetration or different kinds of possible toxicity. This allows us to identify "red flags" extremely early in the pipeline and avoid wasting resources.
We have recently applied these advanced methods to biometrical discovery, and are now bringing this game changing technology to psychedelic medicine.
Our team has a stellar track record, building and exiting AI companies that have superhuman capabilities to detect complex patterns in data.
Dr. Richard Dallaway
Richard's expertise in AI spans 30 years from his undergraduate in AI, and his DPhil in machine learning at the University of Sussex. He has pioneered the use of deep-learning, evolutionary AI, among others, in fields as diverse as drug discovery to cancer diagnostics, to finding patterns in retail consumer purchase data. Richard combines his expertise in machine learning with deep knowledge of building and deploying large-scale enterprise software systems. He is a frequent speaker at conferences and a thought leader in the application of AI in scientific discovery.
Dr. Suran Goonatilake OBE
Suran is a visiting professor at the Computer Science Department at University College London (UCL) and co-founder of several deep-tech companies. He did his Phd in machine learning at UCL and along with fellow students co-founded Searchspace, a company that detects "unusual patterns" in financial transactions (e.g. insider-dealing), which was acquired by Warburg Pincus. He is co-editor of two books, "Intelligent Systems for Finance and Business" and "Intelligent Hybrid Systems". Suran was made an officer of the Order of the British Empire (OBE) in 2005.
Chris has been the co-founder of several tech companies, leading product management in digital media and face recognition offerings. He has business development and consulting experience in pharmaceutical, healthcare and technology industries. His academic background in is Physiological Sciences and Drug Pharmacology from University of Newcastle-upon-Tyne Medical School. As an active public speaker on physical and mental health, Chris is a passionate advocate of psychedelic-assisted psychotherapy with deep knowledge and understanding of the industry.
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