Speeding Innovation: A Primer on AI in Pharma R&D

Robotic Arm carrying a pull bottle. Representing the impact of AI on pharma R&D.
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THE COWEN INSIGHT

Artificial intelligence is starting to make its mark in biopharma. It offers the potential to speed drug R&D, delivering on the promise of data-driven drug development to find better, safer treatments faster. As biopharma companies seek to grow their AI efforts in the coming years, we find platforms built on large, diverse databases supported by wet lab capabilities to be differentiators.

DISRUPTING THE R&D STATUS QUO WITH AI

The current status quo in drug discovery is insufficient. Development times and R&D productivity are too low, repeating tired strategies. Our report is a primer on where we are today, portfolio suggestions & what/who to watch.

We believe working smarter, not harder, is the answer. New drug discovery engines powered by AI are yielding R&D productivity gains today, and we believe high-value drug successes will emerge.

AI OPPORTUNITY IS UNDERAPPRECIATED

We believe investors are not giving credit to or misunderstanding the opportunity as “too early” despite near-universal adoption by pharma, either on their own or via collaborations. We note the $32B + in potential value in AI-driven drug discovery partnerships in the last 2 years.

Near term, we believe AI/Machine Learning will usher in a new era of drug discovery as well as new drug modalities, including those that seek to go after “undruggable” targets. This will manifest in many more drug candidates entering the clinic in the next few years.

This multi-analyst, multi-sector report provides a comprehensive overview of this fast-emerging new field in drug development. We outline:

  • The basics of AI and drug development
  • The history of advances in AI that have led up to this point
  • The growing adoption among biopharma
  • The expansive range of opportunities for AI to speed drug discovery and development

We detail how dozens of biopharma companies are using these tools to quickly identify potential failures or successes, accelerate patient selection for clinical studies, unravel discoveries in disease pathology, and identify novel targets for drug candidates — just to name a few applications.

PROPRIETARY SURVEY OF BIOPHARMA R&D LEADERS

This work is supported by our proprietary survey of biopharma R&D leaders (with significant participation from large pharma and big biotech) who are using AI tools in their drug discovery and development. We’ve also captured input from three KOLs and checks with our covered companies offering AI tools and/or developing drugs supported by proprietary AI platforms. In addition, we’ve also researched and analyzed disclosed AI efforts across pharma, big-cap biotech, and SMID biotech, outlining case studies of how these tools are being used and the key characteristics of what we view as leading and differentiated AI tool providers.

AI ADOPTION BY LARGE PHARMA & BIOTECH COMPANIES

The use of AI in drug development is an exciting space, with many players often making similar claims. This report provides a framework to help investors differentiate and identify high-quality companies that use AI to accelerate drug discovery and improve productivity.

It’s clear to us that the adoption of AI by large pharma and biotechs via collaborations in the last 2 years ($32B+ in potential value) will continue, driven by efficiency gains and increased probabilities of success. Given the annual R&D spend in pharma (almost $100B/ year per CBO data), the economic value of accelerating the time to market by 3-5+ years and extending the number of years before patent expirations is immense. As a result, the level of investments have increased, with ~$7.6B raised by public AI-enabled biotech and tools companies since 2020, with valuations being impacted positively by those using AI in drug discovery.

We expect several private companies to go public in the next few years and continued investments into the space by both investors and Pharma/Biotech, which highlight the tremendous potential upside of AI and in silico approaches to dramatically improve R&D productivity.

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