A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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Amandeep Singh

Contributor Biopharma Insights

Amandeep is a life science consultant at MP Advisors, a biopharma only financial and strategic advisory firm. Amandeep brings 7+ years of experience in preclinical and early-phase drug discovery in infectious diseases and cancer research. He is skilled in data mining and has carried out several market intelligence projects in his previous positions at Siemens healthcare and as a freelancer. Prior to joining MP Advisors, Amandeep worked as an Associate Scientist at Inixium, Canada. He is passionate about the market trends of emerging scientific fields in biopharma as well as med-tech space. Amandeep obtained his PhD in Biophysics from Indian Institute of Science, Bangalore.

   

Artificial Intelligence Machine Learning

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The AI Productivity Game in Pharma

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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.