The ‘AI in Pharma’ start-up space has been rapidly exploding, with new companies being formed every month. The explosion has been fueled by slow yet steady adoption of such tools by the biopharma companies. However, it’s only a handful number of start-ups that are garnering all the attention while others are struggling to even get a single ‘paid’ project.

Several start-ups are likely to die within 2 years, even when they had built great products/ services. A good idea or product is only a small brick in the building of a successful company. Biopharma is a complex industry with several stake holders, high regulations, and high rates of failure dure to complexity of the biological systems. With these limitations in the backdrop, the hesitation to trust or adopt a new tool is justified.

The AI drug discovery industry has already gathered momentum with AI start-ups having signed more than 200 deals with 50+ pharma companies over the last few years, and these are just the disclosed deals. Few top companies like InSilico Medicine and Cyclica claim to have over 100 collaborations each with Academia and Industries. With billions of dollars pouring-in, it is likely to gain further impetus with the industry approaching maturity in the next few years from its formative stage.

A conundrum that has been bothering these groundbreaking start-ups is the business model.

AI companies have been shuffling with their partnership models, having to display high flexibility to tend to the specific requirements of the partners. The roles could range from utilizing AI to develop internal pipelines as a biotech or providing AI as software or AI-driven services like a CRO.

The pharmaceutical business is one of the riskiest industries to venture into. Drug discovery is an artisanal process where a carefully designed drug takes about 10 years and approximately 2.5 billion dollars to be approved and launched into the market. The complexity of biological systems places the odds at a ridiculous failure rate of 90%. In recent years, the declining efficiency of the R&D efforts has put the pharma industry on its toes. 

In the past decade, Artificial Intelligence (AI) has already revolutionized several industries, including automotive, entertainment and fintech. AI dictates routes and ETA on google maps, executes multiple stock exchange transactions, enables facial recognition, and powers the voice assistants Siri and Alexa. However, the adoption of AI in pharma has been restricted due to limited data available about what works (the successful 10%) and the innate complexity of the process of drug discovery.