AI in Pharma R&D: Unlocking the Potential

AI in Pharma R&D: Unlocking the Potential
Written by
John Holodnak
Published on
February 23, 2024

Drug research and development (R&D) is undergoing a profound transformation, propelled by advancements in artificial intelligence (AI) and machine learning (ML). Despite global economic challenges facing the pharmaceutical industry, AI's integration into Pharma R&D holds immense promise. This white paper delves into the pivotal role of AI/ML in reshaping pharmaceutical R&D, specifically through the lens of talent recruitment.

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Current State of Affairs: AI In Pharma R&D

Potential for Increased Efficiency

As evidenced by the daunting industry statistics chronicling the failure of nearly 90% of drug development projects in the clinical phase against the aggregate spend by the world’s top 20 pharmaceutical companies of $139 billion in 2022, researching and developing new drugs is notoriously complex, expensive, unpredictable, and time intensive. And in parallel, there is significant downward global pressure on drug prices. 

Thus, it comes as no surprise that pharmaceutical companies are aggressively seeking ways to improve return on investment (ROI), streamline the drug approval process, and get needed cures to patients faster. Rapidly evolving AI/ML technology offers innovative tools with the potential to enhance the efficiency and cost-effectiveness of each stage of the R&D process. For economic reasons as well as patient outcomes, incorporating and/or developing AI/ML technology has become a strategic imperative for leading companies in the industry.

Applications across the R&D Continuum

Pharma and Biotech companies employ applicable technology in all or some aspects of the R&D process.


To augment drug hunting, predictive modeling can analyze data to identify potential drug targets by efficiently recognizing patterns and relationships. To prioritize and validate targets, diverse data sources—ranging from genomics, clinical trials, electronic health records, and academic research—can be integrated to assess the likelihood of a target’s involvement in a disease.


Molecular compound screening and design are enabled through predictive models analyzing chemical and biological data to guide the design of novel molecules. Depending on the model deployed, predicting the likelihood of the chemical compound binding to a target can be accelerated through virtual screening, generative models, and quantitative structure-activity relationship (QSAR) models. Predictive models can also assess the toxicity of the compounds based on their chemical structure and properties, guiding the selection of safer drug candidates.


Finally, AI/ML can improve the efficiency and efficacy of clinical trials by leveraging predictive models to optimize clinical trial design, patient recruitment, and monitoring. Historical trial data can be analyzed to identify optimal trial restrictions. Diverse patient data, including genetic information and biomarkers, can also be usefully analyzed to identify relevant patient subgroups and support precision medicine.  

Market Trends in Big Pharma & Biotech

The pharmaceutical industry is heavily investing in AI/ML, evident from collaborations between major pharma companies and AI-powered innovators. Notable examples include:

  • $2B collaboration deal between insitro and Bristol Myers Squibb (BMS) in 2020
  • Creation of AION Labs by AstraZeneca, Merck, Pfizer, and Teva in 2021, a collaborative venture to support firms employing AI in drug discovery
  • $610M collaboration between Absci and Merck in 2022, followed by the additional $247M announced in collaboration with Astra Zeneca just this past month
  • $509M strategic collaboration between Phenomic AI and Boehringer Ingelheim
  • $6B acquisition of Nimbus Therapeutic’s highly selective Allosteric TYK2 Inhibitor by Takeda
  • $12B deal between Recursion, Roche and Genentech
  • Multiple Exscientia deals: $674M with Merck KGaA, $5.2B with Bayer, $1.2B with Bristol Myers Squibb, and $5.2B deal with Sanofi
  • Multiple Amgen deals: $1.9B co-investment with Nvidia in Generate:Biomedicines, PostEra AI $1B collaboration

Uncertain financial markets, however, have led to a decrease in venture funding for AI biotech companies since its peak in 2021 across 108 deals. Meanwhile, funding for startups in the generative AI space was up to $21.4B in September of 2023, from $5.1B in 2021.

While computational techniques are not new to drug R&D, the technology and data being analyzed generate excitement and skepticism, the latter being fueled by a lack of immediate results. Quality of data to feed the algorithms is crucial to robust results of AI/ML applications, and levels of quality vary both within the industry overall as well as between biotech companies. As databases are further optimized for AI/ML applications in drug R&D, and as the technology continuously learns, the breadth of the potential of such a rapidly evolving space will become decisively manifest—and the need for the talent behind AI/ML will continue to rise.

The Talent Wars

Despite the increasing demand for AI/ML talent, finding the right “bilingual” professionals with dual expertise in both life sciences and information technology represents an executive selection challenge of significant proportions. Moreover, biotech and major tech companies compete intensely for top talent and AI/ML tool providers. 


That said, biotech and pharma present a distinctly appealing opportunity for AI/ML specialists to contribute valuable insights to cutting-edge research and development while honing relevant skills in an uncommonly collaborative, multi-faceted, purposeful, and stimulating environment.


The key AI/ML roles—Chief Data Officer, CTO, and Chief Platform Officer—are relatively new organizational concoctions and require a learning curve for both recruited executives and hiring managers. Boards, CEOs, Heads of R&D, and Chief Information Officers all need more AI/ML expertise. They must adapt internally and externally to recruit and integrate successfully in an intensely competitive executive labor environment.


Talent, not capital, is scarce in this brave new world.

AI/ML in Action: Occam Relevant Placements

Several of Occam's clients incorporate AI/ML at one or multiple stages of R&D. Below are examples of their pioneering efforts at specific stages. However, with their application of AI/ML technology constantly evolving, these groupings would not reflect the full breadth of their current scope and capabilities.  They should not be read as the sole stage where AI/ML is deployed in the R&D process.



RECURSION (US) Developing drug discovery platforms and pipelines with machine learning.

o   OCCAM RECRUITMENTS: Board Member; COO; Head of Data Science

RELATION (UK) Utilizing machine learning and experimental technologies to discover drugs for pressing unmet needs.


NUCLEOME (UK) Combining 3D genome technology and machine learning to link genes to diseases and map pathways precisely for drug discovery.


ATOMIC AI (US) Exploiting the fusion of artificial intelligence and structural biology to unlock RNA drug discovery.


INSITRO (US) A data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform how medicines are created to help patients.

o   OCCAM RECRUITMENTS: CSO; CDO; Head of People; Head of Neuroscience Discovery; SVP Drug Discovery; SVP Research Operations; VP Molecular Design

ZEBIAI (US) Applying experimental DNA encoded library data sets to power machine learning for drug discovery.


BIOXCEL THERAPEUTICS (US) Utilizing artificial intelligence approaches to develop transformative medicines in neuroscience and immuno-oncology.


DEEP GENOMICS (CANADA) Aims to revolutionize drug development by leveraging expertise in artificial intelligence (AI) to decode RNA biology.




LABGENIUS (UK) The first biopharmaceutical company developing next-generation protein therapeutics using a machine learning-driven evolution engine (EVA™).


CHARM THERAPEUTICS (UK) Delivering transformational medicines through 3D deep learning and cutting-edge drug discovery technologies.


ANAGENEX (US) Evolving new small molecule medicines by combining ultra-high throughput biochemistry and machine learning.

o   OCCAM RECRUITMENTS: Board Member; CSO; Head of Computational Chemistry

CHEMIFY (UK) Digitizing chemistry, including using artificial intelligence to explore the trillions of possible combinations of natural elements in chemical space.


ODYSSEY (US) Combines AI and ML for molecular design with a chemistry platform of covalent libraries targeting multiple amino acids, molecular glues, and natural products.


IKTOS (FRANCE) Specializes in developing AI solutions applied to chemical research, specifically medicinal chemistry and new drug design.


PEPTONE (UK & SWITZERLAND) Developing Oppenheimer, an AI protein-modeling platform that can transform un-druggable intrinsically disordered proteins (IDPs) into developable drug candidates




GSK.AI (US/UK) Using advanced machine learning and AI applications to enhance drug discovery and provide critical insights that increase the probability of success in the clinic.


UNLEARN AI (US) Using generative AI to enhance clinical studies' efficiency, ethics, and reliability.

o   OCCAM RECRUITMENTS: Board Member; Head of People

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