Emerging AI Player in the Drug Discovery Race Promises to Screen Ultra-large Chemical Spaces Within Hours

The multi-directional involvement of artificial intelligence into drug discovery is gradually becoming a routine, starting with the target identification and  having utility at every next step all the way to clinical trials optimization and drug commercialization. It is known that drug discovery is a complex process which may take up to 12-15 years and cost more than $1 billion. This is where AI enters the game with the promise of making the process faster, cheaper and at the same time helping innovate at a different level — discovering previously unknown targets and drug molecules.

In short, the early drug discovery process consists of target and hit identification, which is followed by the lead selection and optimization. In this case hit is an early compound which demonstrates a desired activity in an assay of choice, and lead is the best out of hits. Besides the problem of “undruggable” targets, the hit rate in experimental high-throughput screening (HTS) is very low, typically ranging between 0.01% and 0.14%, to as little as 0.0001% when targeting some protein-protein interactions. AI-based platforms can enhance this process and reduce the costs and time needed.

The Receptor.AI’s platform has been used internally for several months and has shown experimentally validated results in several case studies and ongoing research collaborations. According to the Receptor.AI’s team, their platform was compared with other state-of-the-art algorithms, which confirmed a distinguishably high predictive capability of the developed AI-based platform.