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 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

figures from the Food and Drug Administration.

Furthermore, AI could make drugs safer, increase the success rate of drugs in clinical trials, and lead to the discoveries in areas of therapeutics that were previously unexplored or assumed to be barren. Many adverse side effects or toxicity issues become appar- ent only after clinical trials, at a point where many patients may have already been exposed. AI systems can process and analyze vast sets of data about known compounds to generate predictions and create models to simulate how a new molecule may behave or inter- act in different chemical and physical environments. These models could help us better understand how a new drug might affect different parts of the body. Additionally, for each drug in the market, there are millions of compounds that are nearly chemically identical to it, with distinctions as subtle as an extra hydrogen or double bond. Some of these variations could work better than the approved drug, but are not conceived by chemists. AI could comb through all of these therapeutically promising isomers to search for compelling candidates for further exploration. Similar to the periodic table, these compounds are grouped with neighboring compounds that have related prop- erties, but in multidimensional space. Positions are assigned according to a plethora of characteristics, such as the number of carbon atoms the compound has.

The key to algorithms is that they are agnostic and can be applied to any drug targets. Considering this fact and the heightened efficiency in the drug devel- opment cycle, drugs designed by Exscientia’s AI could be the start of a revolution in the way we develop new treatments for diseases.

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