FIRST DRUG MADE BY AI TESTED IN HUMAN TRIALS By
MELODY LEUNG EJ BECK
The first drug invented entirely by artificial intelligence (AI) is entering phase I clinical trials in March, a key milestone for machine learning in drug discovery. The compound, designed to treat patients with obsessive-compulsive disorder (OCD), was developed by U.K.-based AI startup Exscientia in partnership with the Japanese pharmaceutical company Sumitomo Dainippon Pharma. As opposed to conventional drug development techniques which require around four and a half years, the AI-designed drug completed its research exploratory phase in less than 12 months. Developing new medications is no small feat. It takes many years of labor-intensive research and testing, with even more time devoted to testing its efficacy. Chemists estimate that there are 1060 possible compounds with drug-like characteristics – more than the number of atoms in the Solar System. Therefore, finding potential molecules best suited for a target disease is extremely complex and challenging. Moreover, early-stage drug discovery largely remains a trial-and-error process guided by researchers, and successful findings of a molecule viable in lab settings may not prove successful in patient populations. The confluence of these factors has led to the FDA approving less than 40 new drugs per year, with only around 22 intended for public use. Many decisions are required when finding the right molecules to target a disease. Exscientia’s AI Centaur Chemist uses a host of algorithms to narrow down which chemicals to synthesize and test by learning complex drug design rules from chemists. The
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algorithms sift through tens of millions of potential molecules and catalog, characterize, and compare their properties in computer models and simulations (in silico). Instead of using traditional statistical methods such as linear regression, through machine learning algorithms, AI can recognize complex patterns that are difficult to model using traditional methods and create its own logic. The computer algorithms emulate human cognition by representing data through a vast network of interconnected neurons similar to the human brain, with different weights assigned to each piece of evidence to reach a conclusion. In this fashion, AI can learn a lot faster and process more data than conventional approaches. The AI's selection process resulted in discovering 350 compounds needing to be made and tested, less than a fifth of the typical 2500 candidates. This unprecedented productivity in selecting the best drug candidates has the potential to save vast amounts of time and money. In one study, Insilico Medicine, an AI-directed molecule screening platform from a startup, was compared with the past work of human researchers seeking fibrosis treatment options. While it took the researchers eight years to put forward viable candidates for trials, it only took the AI 21 days. Although further refinements with the algorithm were required to achieve a comparable quality in the drug candidate, this finding demonstrates the overwhelming efficiency and potency of AI. Exscientia estimates that their algorithms could reduce the cost of the drug discovery process by 30% from the current average of $2.7 billion, according to
