This Startup Proposes New Lightweight and Informative Molecular Descriptors for Drug Discovery

Over the last five or so years, the drug discovery industry has started adopting artificial intelligence (AI) at unprecedented scale, with pretty much every big and small pharma company doing some kind of pilots or more substantial projects having some AI component in it – from machine learning algorithms and deep learning networks to natural language processing models. Technology proved to have such a fundamental impact on performance of drug discovery work, that we now see a wave of young companies – sometimes referred to as "digital biotech" – which have a whole new business model revolving around the platform-based process of innovation. Some companies have "end-to-end" drug design platforms capable of automatically doing not only concept creation and target discovery, but also hit discovery, part of lead optimization work, and even predicting clinical trial outputs and identifying clinically-relevant biomarkers.

Receptor.AI is one of the companies at the forefront of "digital biology" movement, having built a modular AI-based discovery platform, aimed at fast and efficient target and lead discovery. While the research has been going on for quite some time, the company has been launched last year and already raised seed round.

I have started this column and will be sharing insights about how our company is re-imagining the field of computational drug design. In this series of posts, I am going to be discussing some of the solutions we have developed and case studies where we demonstrate how our AI system is superiour to legacy approaches, and how it is competing with other players on the market.