Amandeep Singh

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AI and Big Data  

Amandeep is a life science consultant at MP Advisors, a biopharma only financial and strategic advisory firm. He is skilled in business growth strategy and works with several AI life science start-ups to catalyze their globalization journey. He also works with several pharma groups to design and execute successful digitalization and AI implementation strategies. Amandeep obtained his PhD in Biophysics from Indian Institute of Science, Bangalore.

tags:   Artificial Intelligence     Machine Learning    

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Tech Providers or Biotechs: The Quest to Find an Optimal Business Model Continues for AI Drug Discovery Companies…

Tech Providers or Biotechs: The Quest to Find an Optimal Business Model Continues for AI Drug Discovery Companies…

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 AI Productivity Game in Pharma

The AI Productivity Game in Pharma

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.