Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Artificial Intelligence In Drug Discovery Market: Revolutionizing ...
Introduction: The Convergence of AI and Life Sciences
Traditionally, drug discovery is a time-consuming and costly process, often taking 10–15 years and billions of dollars to bring a single drug to market. With a success rate of less than 10% for molecules entering clinical trials, the pharmaceutical industry has long grappled with inefficiency and uncertainty.
Artificial intelligence offers a solution. AI algorithms — especially those based on machine learning (ML) and deep learning (DL) — can process massive datasets, identify hidden patterns, and make predictions at speeds and accuracies that far exceed human capability. From identifying promising molecular targets to optimizing clinical trial design, AI is transforming every stage of the drug discovery pipeline
Key Market Segments
Drug repurposing is an emerging trend — AI helps identify new uses for existing drugs, a faster and less expensive route to market.
Pharma giants such as Pfizer, Novartis, and Merck are heavily investing in AI collaborations, while CROs are adopting AI to boost their service offerings.
Regional Insights
North America:
The leading region in the AI drug discovery space. Home to tech giants, premier academic institutions, and an innovation-driven pharma sector, the U.S. Accounts for the largest market share.
Europe:
Strong presence of pharmaceutical companies in Germany, Switzerland, and the UK. Supportive regulations and AI funding by the European Union are propelling growth.
Asia Pacific:
Emerging as a lucrative region with rising investment in AI healthcare startups in China, India, Japan, and South Korea. The region's large patient population and government initiatives are boosting AI adoption.
Market Drivers
AI shortens the timeline for drug discovery by simulating clinical trials and predicting outcomes faster than traditional methods.
The proliferation of omics data (genomics, proteomics, metabolomics) requires powerful tools like AI to derive actionable insights.
The availability of high-performance computing, cloud platforms, and sophisticated algorithms enables the processing of petabytes of data with precision.
AI enables personalized drug discovery based on individual genetic profiles, a key pillar of precision medicine.
Key Players and Strategic Initiatives
Several companies and collaborations are shaping the future of AI in drug discovery:
Partnerships between pharmaceutical companies and AI firms are on the rise. Notable collaborations include:
These partnerships aim to combine domain expertise with AI capabilities to accelerate innovation.
Challenges Facing the Market
Despite immense potential, several challenges must be addressed:
Inconsistent, unstructured, or biased datasets can lead to inaccurate predictions and false positives.
AI-generated data is still a gray area in regulatory frameworks like the FDA or EMA. Clear guidelines are essential for safe integration.
Determining ownership of AI-generated inventions raises complex legal questions.
The convergence of AI and drug discovery requires interdisciplinary expertise, which remains scarce.
Emerging Trends
AI models like GPT and GANs are being adapted to "generate" novel chemical structures that may not exist in any known database.
Simulated models of human patients can reduce the need for large-scale clinical trials.
Companies are offering AI-based platforms to biotech firms on a subscription basis, democratizing access to powerful tools.
Initiatives like Open Targets and the COVID Moonshot promote open data sharing to accelerate drug discovery.
Case Study: AI and COVID-19
During the COVID-19 pandemic, AI played a pivotal role in accelerating vaccine and therapeutic development. For example:
The pandemic served as a proof-of-concept for the utility of AI in crisis-driven research.
Future Outlook
The Artificial Intelligence in Drug Discovery Market is still in its early stages, but its trajectory points toward becoming a core part of the pharmaceutical R&D process. By 2032, AI is expected to be a standard feature across all phases of drug development.
Key future developments include:
Conclusion
The fusion of artificial intelligence with drug discovery marks one of the most transformative shifts in modern medicine. With the ability to analyze vast datasets, identify drug targets, predict outcomes, and reduce time to market, AI is not just enhancing drug discovery — it is redefining it.
While challenges remain, the benefits far outweigh the hurdles. As technology continues to evolve and regulatory bodies adapt, AI will increasingly become a trusted partner in the journey from molecule to medicine. For pharmaceutical companies, investors, and innovators alike, this is a market too powerful to ignore.
View source: https://www.Amecoresearch.Com/market-report/artificial-intelligence-in-drug-discovery-market-2771023
How Artificial Intelligence Is Influencing Drug Discovery
Both major pharmaceutical companies and startups are applying artificial intelligence in drug discovery. Often the bigger players are linking up and forming partnerships with AI startups.
The drug discovery process is lengthy. Typically, it can take five years from a proposal, based on laboratory experiments, to see a new medicine hitting the market. The major gateposts are experimenting with different active ingredients; running clinical trials; and seeking regulatory approval from global regulatory, like the U.S. Food and Drug Administration.
To a degree, modern drug discovery is faster than before. The sequencing of the human genome, for example, has led to a faster process. This milestone in biological science allowed for rapid cloning and synthesis of large quantities of purified proteins, enabling high throughput screening of large compounds libraries against isolated biological targets.
Nevertheless, the desire for faster concept-to-market has led to pharmaceutical companies investing in artificial intelligence. This investment can be seen with emerging companies and the bigger players.
Start-up examples
Examples of start-ups include the company BioSymetrics, which deploys AI to process raw phenotypic, imaging, drug, and genomic data sets. This process allows scientists to add machine learning capabilities into new compound discoveries.
A second start-up making inroads is the firm Helix, which is using AI to respond to verbal questions and requests in the laboratory setting. This process permits researchers to raise efficiency levels and to keep up-to-date with relevant new research. There is also an added bonus in terms of using the platform to manage inventory.
A third start-up of note is Mozi, which has designed AI to identify patterns in biomedical data and, from this, infer hypotheses for investigation. Here scientists can analyse multiple datasets in the context of global biomedical knowledge. This process is particularly useful for personalized medicine initiatives.
Major players
In terms of major players, AstraZeneca and Berg Health have entered into a partnership designed to discover new therapeutic targets for neurological diseases like Parkinson's. In addition AstraZeneca has announced a collaboration with the company Alibaba to apply technology including artificial intelligence to patient diagnosis and treatment
These examples tally with a TechEmergence study on pharmaceutical sector executives. This showed that 50 percent anticipate broad scale AI adoption by 2025 and around half of the participants anticipate that initial AI applications will target "chronic conditions."
Artificial Intelligence Makes A Splash In Small-molecule Drug Discovery
In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Fig. 1a). And the number of financings and average amount raised soared in 2021.
At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Fig. 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat. Rev. Drug Discov. 21, 175–176; 20221). These pioneering AI-focused companies are the focus of this article, which highlights some of the trends in the field based on recent major company financings and deals.
Fig. 1Recent financings for companies engaged in AI/ML-enabled drug discovery and development. AVenture-capital-backed private financings with disclosed raises for companies from seed stage to series D. BFinancing by disclosed modality when specified; biologics includes peptides, vaccines, antibodies, bispecific molecules and medium-sized macrocycles. Analysis provided by Mavra Nasir, Peter Bak and Ron Thompson at Back Bay Life Science Advisors based on data sourced from Pitchbook on 170 companies categorized as working in drug discovery and development, for the period from 1 January 2015 through to 17 March 2022. *Data for 2022 are partial. AI, artificial intelligence; ML, machine learning.
Spectrum of strategiesThe spectrum of approaches for the application of AI in small-molecule drug discovery can be illustrated by a selection of companies that have raised substantial funding (Table 1) and/or signed major deals with large pharma companies (Table 2) in the past two years. Some of these companies are mainly focused on a particular stage of the drug discovery pipeline, such as target identification or compound screening, while others are aiming to establish end-to-end platforms in which AI tools are at the core in each step.
One of the companies using AI for drug target identification is BenevolentAI, which agreed to go public at a €1.5 billion valuation in Amsterdam through what was billed as Europe's largest ever special purpose acquisition company (SPAC) deal in December 2021 (Table 1). BenevolentAI's approach is based on a knowledge graph that integrates a wide range of publicly available biomedical and chemical data with in-house data, which can be mined with AI tools to generate target hypotheses. For example, the company has been collaborating with AstraZeneca since 2019 on target identification for chronic kidney disease and idiopathic pulmonary fibrosis, and expanded the partnership to include heart failure and systemic lupus erythematosus in January 2022.
Table 1Select recent financings for companies engaged in AI-based small-molecule drug discoveryDate
Company
Headline
July 2020
Relay Therapeutics
Relay raises $460 million in an IPO
August 2020
Atomwise
Atomwise raises $123 million in a series B financing round co-led by B Capital Group and Sanabil
March 2021
Valo Health
Valo Health closes its series B financing round at $300 million, including a $110 million investment from Koch Disruptive Technologies
March 2021
Insitro
Insitro raises $400 million in a series C financing round led by Canada Pension Plan Investment Board
April 2021
Recursion
Recursion raises $436 million in an IPO
June 2021
Insilico Medicine
Insilico Medicine raises $225 million in a series C financing round led by Warburg Pincus
August 2021
XTalPi
XTalPi raises $400 million in a series D financing round co-led by OrbiMed Healthcare Fund Management and HOPU Investments
October 2021
Exscientia
Exscientia raises $510 million from a $350 million IPO and a concurrent $160 million private placement led by SoftBank
December 2021
BenevolentAI
BenevolentAI announces it will merge with Amsterdam-listed Odyssey Acquisition in a deal that is expected to raise around €390 million
A second company that has recently established a major pharma collaboration based on novel target identification is Verge Genomics. In July 2021, Eli Lilly signed a deal potentially worth more than $700 million with Verge focused on identifying targets for amyotrophic lateral sclerosis (ALS) using Verge's AI-enabled platform, which is based on a proprietary collection of patient brain transcriptomes across multiple neurodegenerative diseases.
There are high hopes that AI-derived insights might provide a starting point for therapeutic breakthroughs for neurodegenerative diseases such as ALS, which have seen little medical progress in decades and many clinical trial failures. The AI-driven platform company Insitro is also working on the identification of novel targets for ALS with its partner Bristol Myers Squibb, with which it agreed a deal potentially worth more than $2 billion in October 2020 (Table 2). Insitro has raised substantial financing in the past two years too, with a $400 million series C round in March 2021 following shortly after a $143 million series B round in May 2020.
Insitro was founded in 2018 around a strategy to address one of the key challenges in the application of ML approaches for drug discovery: the quality of the data being analysed by the ML tools (Nat. Rev. Drug Disc. 18, 576–577; 20192). Public datasets can be highly heterogeneous, as well as polluted with erroneous or mislabeled data, and Insitro is generating its own high-quality biological datasets from cellular disease models at high throughput.
Recursion Pharmaceuticals, founded in 2013, is another company with a strong focus on generating bespoke high-quality data from cellular models and applying ML to gain insights that may not be obvious to human experts—for example, by using image-based profiling of cellular disease models treated with a library of potential drug leads (Nat. Rev. Drug Discov. 18, 653–655; 20193). In one of the biggest deals in the field so far in December 2021, with a potential value of $12 billion, Recursion agreed to collaborate with Genentech and Roche to use its AI-guided high-content screening platform to identify novel targets and medicines for up to 40 research programs in neuroscience and oncology (Table 2). This came just a few months after Recursion raised $436 million in an upsized IPO in April 2021 (Table 1).
Exscientia, a pioneer in applying AI to small-molecule drug design, had a hefty IPO in 2021 as well, raising $510 million in October from a $350 million IPO and a $160 million concurrent private placement led by Softbank (Table 1). This followed on the heels of a $100 million series C round in March 2021 and a $225 million series D round in April 2021 led by Softbank Vision Fund 2, with the potential to access a further $300 million at Exscientia's discretion. The company has raised substantial funding through deals too. In May 2021, it signed a potential $1.2 billion deal with Bristol Myers Squibb to discover small-molecule drug candidates in areas including oncology and immunology, followed by a potential $5.2 billion deal with Sanofi in January 2022 focused on oncology and immunology, with the two deals together bringing in a total of $150 million in upfront payments (Table 2).
Table 2Select recent AI-based small-molecule drug discovery partnerships with disclosed termsDate
Licensor
Licensee
Deal summary
September 2020
Recursion
Bayer
Recursion and Bayer establish collaboration to discover small-molecule drug candidates for fibrotic diseases by using Recursion's AI-guided screening platform and Bayer's small-molecule compound library. Recursion will receive an upfront payment of $30 million, as well as a $50 million equity investment as part of Recursion's series D financing round, and is eligible for potential milestone payments of $100 million per program for more than 10 programs.
October 2020
Insitro
Bristol Myers Squibb
Insitro partners with Bristol Myers Squibb to use its AI-supported screening platform in the identification of novel targets and candidate drugs for amyotrophic lateral sclerosis and frontotemporal dementia. Insitro will receive an upfront payment of $50 million and is eligible for more than $2 billion in potential milestone payments.
November 2020
Schrödinger
Bristol Myers Squibb
Schrödinger announces a collaboration with Bristol Myers Squibb that will use Schrödinger's physics-based computational drug discovery capabilities to identify small-molecule drugs for targets in oncology, immunology and neurological disorders. Schrödinger will receive $55 million upfront and is eligible for up to $2.7 billion in potential milestone payments.
December 2020
Relay Therapeutics
Genentech
Relay signs deal with Genentech to partner on the development of RLY-1971, an inhibitor of SHP2 identified using Relay's platform, which harnesses AI to analyse protein dynamics. Genentech will pay $75 million upfront and up to $720 million in potential milestone payments, and will take responsibility for the clinical development of RLY-1971, including with Genentech's investigational KRAS inhibitor GDC-6036.
May 2021
Exscientia
Bristol Myers Squibb
Exscientia enters collaboration with Bristol Myers Squibb to discover small-molecule drug candidates in therapeutic areas including oncology and immunology. Bristol Myers Squibb will provide up to $50 million in upfront funding, and potential milestone payments could take the total value of the deal to more than $1.2 billion.
July 2021
Verge Genomics
Eli Lilly
Verge Genomics partners with Lilly to discover novel therapies for amyotrophic lateral sclerosis using its AI-driven platform. Verge will receive up to $25 million in upfront, equity investment and potential near-term payments, and is eligible for up to $694 million in additional milestone payments.
December 2021
Recursion
Genentech/Roche
Recursion collaborates with Roche and Genentech to use Recursion's AI-guided high-content screening platform to identify novel targets and medicines in key areas of neuroscience, as well as an oncology indication. Recursion will receive an upfront payment of $150 million and potential milestone payments of $300 million each for up to 40 research programs.
January 2022
Insilico Medicine
Fosun Pharma
Insilico Medicine enters collaboration with Fosun Pharma to work on four undisclosed disease targets, and Fosun will also co-develop Insilico's QPCTL inhibitor program in immuno-oncology. The deal includes an upfront payment of $13 million to Insilico Medicine, as well as an undisclosed equity investment and potential milestones.
January 2022
Exscientia
Sanofi
Sanofi partners with Exscientia to develop up to 15 drug candidates in oncology and immunology using Exscientia's AI-based personalized medicine platform. Exscientia will receive an upfront payment of $100 million and potential milestone payments worth up to a total of $5.2 billion.
Towards the clinicThe AI strategy being applied by Exscientia, in which vast virtual libraries of small molecules are computationally analysed based on various characteristics such as predicted specificity for particular drug targets in order to identify a small subset to test in lab experiments, has recently led to multiple drug candidates entering clinical trials.
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