It takes over 10 years to bring a new drug to the market. As of 2014, according to Tufts Center for the Study of Drug Development (CSDD), the cost of developing a new prescription drug that gains market approval is approximately $2.6 billion. This is 145% higher compared to what was reported in 2003.
Investing billions of dollars does not guarantee success, and lost time is critical to compensate. The Covid-19 pandemic has reiterated the challenges ahead, and AI represents the best bet to shift towards data-centric drug discovery that is faster and more accurate.
Applications of AI to Address Pharma Challenges
- Target Discovery and Early Drug Discovery
- Design and Processing of Preclinical Experiments
- Clinical Trials
- Repurposing of Existing Drugs
- Aggregation and Synthesis of Information
Competition Landscape:
As per Deep Knowledge Analytics, approximately 395 AI companies, 100 corporations, and 1000 investors are active in the space. Plentiful financing and multiple pharma partnerships illustrate the burgeoning interest in applying AI for drug discovery.
Major Players in AI Drug Discovery:
Investment in AI Drug Discovery
In recent years, the AI drug development industry has gained much attention from investors, venture capital firms, and corporate investment funds, now culminating in total investment in the sector exceeding $13B. Funding for artificial intelligence in drug development hit $4.1B in 2021, a 36% increase compared to 2020.
Succeeding in AI Drug Discovery
- Specialised and specific intelligence in the field of drug discovery
- Creating value out of data.
- Creating impactful collaborations to have access to data, technology and financial resources.
Sources: Deep Knowledge Analytics, Nature, Emersion Insights, The Science Advisory Board
SOURAV RAINA 