How Big Pharma Adopts AI To Boost Drug Discovery

by Andrii Buvailo, PhD          Biopharma insight / Biopharma Insights

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Topics: Emerging Technologies   
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(Last updated: July 2023)

The type of artificial intelligence (AI) which scares business leaders, experts, and activists all over the world, is called “general artificial intelligence"—the  one which could “think” pretty much like humans do, and which could quickly evolve into a dangerous “superintelligence”. There is a notion that it might be invented in the nearest decades, but today we are definitely not there yet. However, with the recent groundbreaking advances in deep learning and natural language processing technologies, particularly -- large language models (LLMs), we have all felt that the world might indeed be changing more rapidly than AI deniers used to think. Let’s face it, only few could foresee such an alarmingly efficient public release of the most generalized AI model of all time -- ChatGPT, by OpenAI. Adding more to that, a race of LLMs has begun, with Google launching Bard, and other companies following the path. 

Even notoriously slow for technology adoption, the pharmaceutical industry has seen accelerated integration of various AI technologies over the last decade, and the interest is rapidly growing. The potential impacts of this transformation extend beyond healthcare providers and patients grappling with difficult-to-treat ailments, reaching into the biotech sector as well. Based on projections from Morgan Stanley Research, even slight enhancements in early-stage drug development success rates, facilitated by artificial intelligence and machine learning, might result in an additional 50 innovative treatments over the next decade. This could equate to a market opportunity exceeding $50 billion. 

According to a 2022 report by GlobalData, 50% of professionals within the healthcare industry would prioritize investments in AI over other emerging technologies, such as big data (38%), digital media (37%), cloud computing (31%), real-world evidence, RWE (27%), and others. 

The 2022 thematic research report titled 'Artificial Intelligence (AI) in Drug Discovery' from GlobalData predicts that the total expenditure on AI by the pharmaceutical sector is projected to escalate to more than $3 billion by 2025.

Let’s review specific examples of how AI is used in the pharmaceutical industry. 

(Since most AI-driven companies use a mix of different approaches and rely on interdisciplinary sources of data for their modeling work, the below classification of AI use cases is illustrative.)

 

AI for drug target discovery and disease modeling

 

One of the most promising areas of AI in pharma is modeling biological systems, and identifying novel drug targets. A number of AI companies, such as CytoReason, are specifically focused on building advanced disease models, for example.

In March 2023, AstraZeneca presented preclinical data on an AI-generated target, the Serum Response Factor (SRF), for idiopathic pulmonary fibrosis (IPF) -- from its collaboration with UK-based AI company BenevolentAI. The target, discovered via BenevolentAI's AI-enabled drug discovery engine, underwent thorough experimental validation by AstraZeneca, involving CRISPR screening in primary human lung fibroblasts and validation via SRF gene silencing or pharmacological SRF pathway inhibition. The presented data indicates that inhibiting SRF-driven transcription of pro-fibrotic genes in lung fibroblasts could potentially lead to antifibrotic efficacy in IPF. To date, the collaboration between BenevolentAI and AstraZeneca has resulted in five AI-generated targets selected for portfolio entry, three of which are for IPF. This successful partnership was expanded in January 2022 for another three years, including two new disease areas - systemic lupus erythematosus and heart failure.

Several months earlier, AstraZeneca announced a strategic research collaboration with Illumina, a global pioneer in DNA sequencing and array-based technologies. This collaboration aims to expedite drug target discovery by melding their respective competencies in AI-based genome interpretation and genomic analysis. The initiative will examine if a unified approach utilizing these technologies can bolster the efficiency and certainty of target discovery in pursuit of promising drugs built upon human omics insights. AstraZeneca's Centre for Genomics Research will adopt a framework merging the AI-based tools of both companies, leveraging next-generation AI interpretation tools like Illumina's PrimateAI and SpliceAI, along with AstraZeneca's own tools such as JARVIS and in silico predictors.

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Comments:

  • Ed Addison 2017-08-08 11:32

    A couple of links provide additional information not here addressed:

    https://www.aiinpharma.com/blog

    https://disruptordaily.com/top-10-artificial-intelligence-companies-disrupting-pharmaceutical-industry/

    reply
    • BiopharmaTrend 2017-08-14 11:44

      Thank you for the valuable updates! These links will be included in the coming reviews.

      reply
  • Aman 2017-08-10 20:35

    Thanks, interesting read.

    I am surprised that InveniAI (off shoot of a company called BioXcel Corporation) never made it your list. InveniAI has been successful not only with Pharma partnerships (Takeda, Alnylam, Axcella, Centrexion all announced in public domain) but also in spinning out drug companies, "BioXcel Therapeutics, 2 Phase II programs", spin out with another large pharma.

    Aman

    reply
    • BiopharmaTrend 2017-08-14 11:48

      Dear Aman, thank you for this additional and valuable information! I will include InveniAI in the upcoming review update at the end of the year. This commentary will make the next review much more informative, indeed. Regards, Andrii

      reply
  • Joe Donahue 2017-08-15 23:30

    Great article. Thank you. This is a rapidly evolving space - both with horizontal AI technologies being applied to pharma discovery as well as a wave of companies - Vyasa is one that didn't make your list - that are focused on on leveraging their deep knowledge of the life sciences vertical.

    reply
    • BiopharmaTrend 2017-08-16 10:39

      Dear Joe, thank you for updating the current list with a valuable contribution. In fact, commentaries here are valuable for our readers and sometimes even more insightful than the article itself.

      reply
  • Manuel GEA 2018-02-05 18:43

    Hello,

    Thank you for this great article, but there is a class of algorithms missing. The Augmented Human Intelligence that combines “horizontal capacity” of Human Intelligence and the vertical capacity of Artificial Intelligence that do not need to be too complicated. We would be happy if you add our company to your survey.

    Bio-Modeling Systems is the world’s first Mechanisms-Based medicine company that changed the discovery paradigm to create novel robust medical meanings from unreliable heterogeneous sources of data to generate validated & directly exploitable first in class heuristic non-mathematical mechanistic CADI™* models.

    We propose to R&D & Translational Medicine Executives, robust alternative decision-making to de-risk, save time, costs, and novel cost-effective diagnostics/therapies for their businesses.

    Created in 2004, profitable since 2006, thanks to our recurrent clients, we confirmed in Pharma, Biotech, Cosmetics, Nutrition and digital-Health, CADI™ Discovery capability to achieve:

    1. A world's first in neurodegenerative diseases (publication) with CEA: 2 awards in the US & Europe.

    2. Pherecydes-Pharma: BMSystems' spin-off, MR infections therapies, 3 patents, publication, world's first multi-centric clinical trial with bacteriophages in Phase I/II, Compassionate Use Success.

    3. CEA/BMSystems collaborative research in CNS that led to the co-owned patent WO201029131with a worldwide exclusive license to CEA’s spin-off currently in Phase II, and

    4. 14 CADI™ successes independently validated by our clients/partners.

    We warmly invite you to download our management summary, our Short corporate Presentation or our CADI™ Discovery Concept and POCs Presentation.

    Best regards

    Manuel GEA

    reply
  • Rodrigo Antonio Faccioli 2018-02-28 17:41

    Thanks for sharing this excellent article. It shows some areas in which AI can be useful for drug discovery.

    reply
  • Max 2018-03-08 12:33

    A really informative post on artificial intelligence. Indeed, Health sector is going to see a boom in near future due to blessings of AI. Companies like Enlitic and IBM are also working on AI to improve serious health diseases. Great post.

    reply
  • Aron Maxwell 2019-03-07 06:51

    Great Article! It's really a valuable Information present on the blog. I have seen artificial intelligence for all other services like recruitment, Housing, Financing etc. I am really surprised by seeing the Artificial Intelligence for biopharma. Keep sharing, such a wonderful Information on the blog.

    reply
  • Chooch 2020-03-06 10:30

    Your blog is really good, It is really helpful and I would like to share your information. If anyone wants to learn AI at an advanced level. Chooch is an AI-Based Company that provides you training for AI Startup, visual search, image recognition solutions, and AIoT. Free AI Demos From our Experts.

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  • Niitesh Pattiil 2021-09-23 18:42

    Hi Andrii

    This is a great compilation however It misses on certain points like suppy chain, data management and also Lob Of The Future concept which is heavily based on use of AI. you can find more information on that in below link

    https://www.p360.com/data360/2021-rings-in-a-new-era-for-innovation-within-the-pharmaceutical-industry/

    reply
    • BiopharmaTrend 2021-10-10 21:17

      Thank you, Niitesh, valuable addition.

      reply
  • john williams 2022-02-08 05:24

    This is John Williams from Pharma Meet 2022

    The Organizing Committee is pleased to invite you to participate in the “21st Annual Meet on Pharmaceutical Sciences” to be held on May 10-11, 2022, Yokohama, Japan, on the theme "Advances in Drug Design, Development and New Nanotechnologies - Current and Future Perspectives".

    For more details visit: https://drugs.pharmaceuticalconferences.com/

    Email: jw480086@gmail.com

    reply

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